System and method for ditributed input-distributed output wireless communications

ABSTRACT

A system for dynamically adapting the communication characteristics of a multiple antenna system (MAS) with multi-user (MU) transmissions (defined with the acronym MU-MAS), such as a distributed-input distributed-output (DIDO) communication system. For example, a system according to one embodiment of the invention comprises: one or more coding modulation units to encode and modulate information bits for each of a plurality of wireless client devices to produce encoded and modulated information bits; one or more mapping units to map the encoded and modulated information bits to complex symbols; and a MU-MAS or DIDO configurator unit to determine a subset of users and a MU-MAS or DIDO transmission mode based on channel characterization data obtained through feedback from the wireless client devices and to responsively control the coding modulation units and mapping units.

CLAIM TO PRIORITY

This application is a continuation in part of application Ser. No.10/902,978 filed Jul. 30, 2004.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to the field of communication systems.More particularly, the invention relates to a system and method fordistributed input-distributed output wireless communications usingspace-time coding techniques.

2. Description of the Related Art

Space-Time Coding of Communication Signals

A relatively new development in wireless technology is known as spatialmultiplexing and space-time coding. One particular type of space-timecoding is called MIMO for “Multiple Input Multiple Output” becauseseveral antennas are used on each end. By using multiple antennas tosend and receive, multiple independent radio waves may be transmitted atthe same time within the same frequency range. The following articlesprovide an overview of MIMO:

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 21, NO. 3, APRIL2003: “From Theory to Practice: An Overview of MIMO Space-Time CodedWireless Systems”, by David Gesbert, Member, IEEE, Mansoor Shafi,Fellow, IEEE, Da-shan Shiu, Member, IEEE, Peter J. Smith, Member, IEEE,and Ayman Naguib, Senior Member, IEEE.

IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 50, NO. 12, DECEMBER 2002:“Outdoor MIMO Wireless Channels: Models and Performance Prediction”,David Gesbert, Member, IEEE, Helmut Bölcskei, Member, IEEE, Dhananjay A.Gore, and Arogyaswami J. Pauiraj, Fellow, IEEE.

Fundamentally, MIMO technology is based on the use of spatiallydistributed antennas for creating parallel spatial data streams within acommon frequency band. The radio waves are transmitted in such a waythat the individual signals can be separated at the receiver anddemodulated, even though they are transmitted within the same frequencyband, which can result in multiple statistically independent (i.e.effectively separate) communications channels. Thus, in contrast tostandard wireless communication systems which attempt to inhibitmulti-path signals (i.e., multiple signals at the same frequency delayedin time, and modified in amplitude and phase), MIMO can rely onuncorrelated or weakly-correlated multi-path signals to achieve a higherthroughput and improved signal-to-noise ratio within a given frequencyband. By way of example, MIMO technology achieves much higher throughputin comparable power and signal-to-noise ratio (SNR) conditions where aconventional non-MIMO system can achieve only lower throughput. Thiscapability is described on Qualcomm Incorporated's (Qualcomm is one ofthe largest providers of wireless technology) website on a page entitled“What MIMO Delivers” athttp://www.cdmatech.com/products/what_mimo_delivers.jsp: “MIMO is theonly multiple antenna technique that increases spectral capacity bydelivering two or more times the peak data rate of a system per channelor per MHz of spectrum. To be more specific, for wireless LAN or Wi-Fi®applications QUALCOMM's fourth generation MIMO technology deliversspeeds of 315 Mbps in 36 MHz of spectrum or 8.8 Mbps/MHz. Compare thisto the peak capacity of 802.11a/g (even with beam-forming or diversitytechniques) which delivers only 54 Mbps in 17 MHz of spectrum or 3.18Mbps/MHz.”

MIMO systems typically face a practical limitation of fewer than 10antennas per device (and therefore less than 10× throughput improvementin the network) for several reasons:

1. Physical limitations: MIMO antennas on a given device must havesufficient separation between them so that each receives a statisticallyindependent signal. Although MIMO throughput improvements can be seenwith antenna spacing of even fractions of the wavelength,

the efficiency rapidly deteriorates as the antennas get closer,resulting in lower MIMO throughput multipliers.

See, for example, the following references:

-   [1] D.-S. Shiu, G. J. Foschini, M. J. Gans, and J. M. Kahn, “Fading    correlation and its effect on the capacity of multielement antenna    systems,” IEEE Trans. Comm., vol. 48, no. 3, pp. 502-513, March    2000.-   [2] V. Pohl, V. Jungnickel, T. Haustein, and C. von Helmolt,    “Antenna spacing in MIMO indoor channels,” Proc. IEEE Veh. Technol.    Conf., vol. 2, pp. 749-753, May 2002.-   [3] M. Stoytchev, H. Safar, A. L. Moustakas, and S. Simon, “Compact    antenna arrays for MIMO applications,” Proc. IEEE Antennas and Prop.    Symp., vol. 3, pp. 708-711, July 2001.-   [4] A. Forenza and R. W. Heath Jr., “Impact of antenna geometry on    MIMO communication in indoor clustered channels,” Proc. IEEE    Antennas and Prop. Symp., vol. 2, pp. 1700-1703, June 2004.

Also, for small antenna spacing, mutual coupling effects may degrade theperformance of MIMO systems.

See, for example, the following references:

-   [5] M. J. Fakhereddin and K. R. Dandekar, “Combined effect of    polarization diversity and mutual coupling on MIMO capacity,” Proc.    IEEE Antennas and Prop. Symp., vol. 2, pp. 495-498, June 2003.-   [7] P. N. Fletcher, M. Dean, and A. R. Nix, “Mutual coupling in    multi-element array antennas and its influence on MIMO channel    capacity,” IEEE Electronics Letters, vol. 39, pp. 342-344, February    2003.-   [8] V. Jungnickel, V. Pohl, and C. Von Helmolt, “Capacity of MIMO    systems with closely spaced antennas,” IEEE Comm. Lett., vol. 7, pp.    361-363, August 2003.-   [10] J. W. Wallace and M. A. Jensen, “Termination-dependent    diversity performance of coupled antennas: Network theory analysis,”    IEEE Trans. Antennas Propagat., vol. 52, pp. 98-105, January 2004.-   [13] C. Waldschmidt, S. Schulteis, and W. Wiesbeck, “Complete RF    system model for analysis of compact MIMO arrays,” IEEE Trans. on    Veh. Technol., vol. 53, pp. 579-586, May 2004.-   [14] M. L. Morris and M. A. Jensen, “Network model for MIMO systems    with coupled antennas and noisy amplifiers,” IEEE Trans. Antennas    Propagat., vol. 53, pp. 545-552, January 2005.

Moreover, as the antennas are crowded together, the antennas typicallymust be made smaller, which can impact the antenna efficiency as well.

See, for example, the following reference

-   [15] H. A. Wheeler, “Small antennas,” IEEE Trans. Antennas    Propagat., vol. AP-23, n. 4, pp. 462-469, July 1975.-   [16] J. S. McLean, “A re-examination of the fundamental limits on    the radiation Q of electrically small antennas,” IEEE Trans.    Antennas Propagat., vol. 44, n. 5, pp. 672-676, May 1996.

Finally, with lower frequencies and longer wavelengths, the physicalsize of a single MIMO device can become unmanageable. An extreme exampleis in the HF band, where MIMO device antennas may have to be separatedfrom each other by 10 meters or more.

2. Noise limitations. Each MIMO receiver/transmitter subsystem producesa certain level of noise. As more and more of these subsystems areplaced in close proximity to each other, the noise floor increases.Meanwhile, as increasingly more distinct signals need to bedistinguished from each other in a many-antenna MIMO system, anincreasingly lower noise floor is required.

3. Cost and power limitations. Although there are MIMO applicationswhere cost and power consumption are not an issue, in a typical wirelessproduct, both cost and power consumption are critical constraints indeveloping a successful product. A separate RF subsystem is required foreach MIMO antenna, including separate Analog-to-Digital (A/D) andDigital-to-Analog (D/A) converters. Unlike many aspects of digitalsystems which scale with Moore's Law (an empirical observation, made byIntel co-founder Gordon Moore, that the number of transistors on anintegrated circuit for minimum component cost doubles about every 24months; source: http://www.intel.com/technology/mooreslaw/), suchanalog-intensive subsystems typically have certain physical structuralsize and power requirements, and scale in cost and power linearly. So, amany-antenna MIMO device would become prohibitively expensive and powerconsumptive compared to a single-antenna device.

As a result of the above, most MIMO systems contemplated today are onthe order of 2-to-4 antennas, resulting in a 2-to-4× increase inthroughput, and some increase in SNR due to the diversity benefits of amulti-antenna system. Up to 10 antenna MIMO systems have beencontemplated (particularly at higher microwave frequencies due toshorter wavelengths and closer antenna spacing), but much beyond that isimpractical except for very specialized and cost-insensitiveapplications.

Virtual Antenna Arrays

One particular application of MIMO-type technology is a virtual antennaarray. Such a system is proposed in a research paper presented atEuropean Cooperation in the field of Scientific and Technical Research,EURO-COST, Barcelona, Spain, Jan. 15-17, 2003: Center forTelecommunications Research, King's College London, UK: “A step towardsMIMO: Virtual Antenna Arrays”, Mischa Dohler & Hamid Aghvami.

Virtual antenna arrays, as presented in this paper, are systems ofcooperative wireless devices (such as cell phones), which communicateamongst each other (if and when they are near enough to each other) on aseparate communications channel than their primary communicationschannel to the their base station so as to operate cooperatively (e.g.if they are GSM cellular phones in the UHF band, this might be a 5 GHzIndustrial Scientific and Medical (ISM) wireless band). This allowssingle antenna devices, for example, to potentially achieve MIMO-likeincreases in throughput by relaying information among several devices inrange of each other (in addition to being in range of the base station)to operate as if they are physically one device with multiple antennas.

In practice, however, such a system is extremely difficult to implementand of limited utility. For one thing, there are now a minimum of twodistinct communications paths per device that must be maintained toachieve improved throughput, with the second relaying link often ofuncertain availability. Also, the devices are more expensive, physicallylarger, and consume more power since they have at a minimum a secondcommunications subsystem and greater computational needs. In addition,the system is reliant on very sophisticated real-time of coordination ofall devices, potentially through a variety of communications links.Finally, as the simultaneous channel utilization (e.g. the simultaneousphone call transmissions utilizing MIMO techniques) grows, thecomputational burden for each device grows (potentially exponentially aschannel utilization increases linearly), which may very well beimpractical for portable devices with tight power and size constraints.

SUMMARY OF THE INVENTION

A system for dynamically adapting the communication characteristics of amultiple antenna system (MAS) with multi-user (MU) transmissions (forthe time being, defined with the acronym MU-MAS), such as adistributed-input distributed-output (DIDO) communication system. Forexample, a system according to one embodiment of the inventioncomprises: one or more coding modulation units to encode and modulateinformation bits for each of a plurality of wireless client devices toproduce encoded and modulated information bits; one or more mappingunits to map the encoded and modulated information bits to complexsymbols; and a DIDO configurator unit to determine a subset of users anda DIDO transmission mode based on channel characterization data obtainedthrough feedback from the wireless client devices and to responsivelycontrol the coding modulation units and mapping units.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained from thefollowing detailed description in conjunction with the drawings, inwhich:

FIG. 1 illustrates a prior art MIMO system.

FIG. 2 illustrates an N-antenna Base Station communicating with aplurality of Single-antenna Client Devices.

FIG. 3 illustrates a three Antenna Base Station communicating with threeSingle-Antenna Client Devices

FIG. 4 illustrates training signal techniques employed in one embodimentof the invention.

FIG. 5 illustrates channel characterization data transmitted from aclient device to a base station according to one embodiment of theinvention.

FIG. 6 illustrates a Multiple-Input Distributed-Output (“MIDO”)downstream transmission according to one embodiment of the invention.

FIG. 7 illustrates a Multiple-Input Multiple Output (“MIMO”) upstreamtransmission according to one embodiment of the invention.

FIG. 8 illustrates a base station cycling through different clientgroups to allocate throughput according to one embodiment of theinvention.

FIG. 9 illustrates a grouping of clients based on proximity according toone embodiment of the invention.

FIG. 10 illustrates an embodiment of the invention employed within anNVIS system.

FIG. 11 illustrates an embodiment of the DIDO transmitter with I/Qcompensation functional units.

FIG. 12 a DIDO receiver with I/Q compensation functional units.

FIG. 13 illustrates one embodiment of DIDO-OFDM systems with I/Qcompensation.

FIG. 14 illustrates one embodiment of DIDO 2×2 performance with andwithout I/Q compensation.

FIG. 15 illustrates one embodiment of DIDO 2×2 performance with andwithout I/Q compensation.

FIG. 16 illustrates one embodiment of the SER (Symbol Error Rate) withand without I/Q compensation for different QAM constellations.

FIG. 17 illustrates one embodiment of DIDO 2×2 performances with andwithout compensation in different user device locations.

FIG. 18 illustrates one embodiment of the SER with and without I/Qcompensation in ideal (i.i.d. (independent and identically-distributed))channels.

FIG. 19 illustrates one embodiment of a transmitter framework ofadaptive DIDO systems.

FIG. 20 illustrates one embodiment of a receiver framework of adaptiveDIDO systems.

FIG. 21 illustrates one embodiment of a method of adaptive DIDO-OFDM.

FIG. 22 illustrates one embodiment of the antenna layout for DIDOmeasurements.

FIG. 23 illustrates embodiments of array configurations for differentorder DIDO systems.

FIG. 24 illustrates the performance of different order DIDO systems.

FIG. 25 illustrates one embodiment of the antenna layout for DIDOmeasurements.

FIG. 26 illustrates one embodiment of the DIDO 2×2 performance with4-QAM and FEC rate ½ as function of the user device location.

FIG. 27 illustrates one embodiment of the antenna layout for DIDOmeasurements.

FIG. 28 illustrates how, in one embodiment, DIDO 8×8 yields larger SEthan DIDO 2×2 for lower TX power requirement.

FIG. 29 illustrates one embodiment of DIDO 2×2 performance with antennaselection.

FIG. 30 illustrates average bit error rate (BER) performance ofdifferent DIDO precoding schemes in i.i.d. channels.

FIG. 31 illustrates the signal to noise ratio (SNR) gain of ASel as afunction of the number of extra transmit antennas in i.i.d. channels.

FIG. 32 illustrates the SNR thresholds as a function of the number ofusers (M) for block diagnalization (BD) and ASel with 1 and 2 extraantennas in i.i.d. channels.

FIG. 33 illustrates the BER versus per-user average SNR for two userslocated at the same angular direction with different values of AngleSpread (AS).

FIG. 34 illustrates similar results as FIG. 33, but with higher angularseparation between the users.

FIG. 35 plots the SNR thresholds as a function of the AS for differentvalues of the mean angles of arrival (AOAs) of the users.

FIG. 36 illustrates the SNR threshold for an exemplary case of fiveusers.

FIG. 37 provides a comparison of the SNR threshold of BD and ASel, with1 and 2 extra antennas, for two user case.

FIG. 38 illustrates similar results as FIG. 37, but for a five usercase.

FIG. 39 illustrates the SNR thresholds for a BD scheme with differentvalues of AS.

FIG. 40 illustrates the SNR thresholds in spatially correlated channelswith AS=0.1° for BD and ASel with 1 and 2 extra antennas.

FIG. 41 illustrates the computation of the SNR thresholds for two morechannel scenarios with AS=5°.

FIG. 42 illustrates the computation of the SNR thresholds for two morechannel scenarios with AS=10°.

FIGS. 43-44 illustrate the SNR thresholds as a function of the number ofusers (M) and angle spread (AS) for BD and ASel schemes, with 1 and 2extra antennas, respectively.

FIG. 45 illustrates a receiver equipped with frequency offsetestimator/compensator.

FIG. 46 illustrates DIDO 2×2 system model according to one embodiment ofthe invention.

FIG. 47 illustrates a method according to one embodiment of theinvention.

FIG. 48 illustrates SER results of DIDO 2×2 systems with and withoutfrequency offset.

FIG. 49 compares the performance of different DIDO schemes in terms ofSNR thresholds.

FIG. 50 compares the amount of overhead required for differentembodiments of methods.

FIG. 51 illustrates a simulation with a small frequency offset off_(max)=2 Hz and no integer offset correction.

FIG. 52 illustrates results when turning off the integer offsetestimator.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however, toone skilled in the art that the present invention may be practicedwithout some of these specific details. In other instances, well-knownstructures and devices are shown in block diagram form to avoidobscuring the underlying principles of the invention.

FIG. 1 shows a prior art MIMO system with transmit antennas 104 andreceive antennas 105. Such a system can achieve up to 3× the throughputthat would normally be achievable in the available channel. There are anumber of different approaches in which to implement the details of sucha MIMO system which are described in published literature on thesubject, and the following explanation describes one such approach.

Before data is transmitted in the MIMO system of FIG. 1, the channel is“characterized.” This is accomplished by initially transmitting a“training signal” from each of the transmit antennas 104 to each of thereceivers 105. The training signal is generated by the coding andmodulation subsystem 102, converted to analog by a D/A converter (notshown), and then converted from baseband to RF by each transmitter 103,in succession. Each receive antenna 105 coupled to its RF Receiver 106receives each training signal and converts it to baseband. The basebandsignal is converted to digital by a D/A converter (not shown), and thesignal processing subsystem 107 characterizes the training signal. Eachsignal's characterization may include many factors including, forexample, phase and amplitude relative to a reference internal to thereceiver, an absolute reference, a relative reference, characteristicnoise, or other factors. Each signal's characterization is typicallydefined as a vector that characterizes phase and amplitude changes ofseveral aspects of the signal when it is transmitted across the channel.For example, in a quadrature amplitude modulation (“QAM”)-modulatedsignal the characterization might be a vector of the phase and amplitudeoffsets of several multipath images of the signal. As another example,in an orthogonal frequency division multiplexing (“OFDM”)-modulatedsignal, it might be a vector of the phase and amplitude offsets ofseveral or all of the individual sub-signals in the OFDM spectrum.

The signal processing subsystem 107 stores the channel characterizationreceived by each receiving antenna 105 and corresponding receiver 106.After all three transmit antennas 104 have completed their trainingsignal transmissions, then the signal processing subsystem 107 will havestored three channel characterizations for each of three receivingantennas 105, resulting in a 3×3 matrix 108, designated as the channelcharacterization matrix, “H.” Each individual matrix element H_(i,j) isthe channel characterization (which is typically a vector, as describedabove) of the training signal transmission of transmit antenna 104 i asreceived by the receive antenna 105 j.

At this point, the signal processing subsystem 107 inverts the matrix H108, to produce H⁻¹, and awaits transmission of actual data fromtransmit antennas 104. Note that various prior art MIMO techniquesdescribed in available literature, can be utilized to ensure that the Hmatrix 108 can be inverted.

In operation, a payload of data to be transmitted is presented to thedata Input subsystem 100. It is then divided up into three parts bysplitter 101 prior to being presented to coding and modulation subsystem102. For example, if the payload is the ASCII bits for “abcdef,” itmight be divided up into three sub-payloads of ASCII bits for “ad,”“be,” and “cf” by Splitter 101. Then, each of these sub-payloads ispresented individually to the coding and modulation subsystem 102.

Each of the sub-payloads is individually coded by using a coding systemsuitable for both statistical independence of each signal and errorcorrection capability. These include, but are not limited toReed-Solomon coding, Viterbi coding, and Turbo Codes. Finally, each ofthe three coded sub-payloads is modulated using an appropriatemodulation scheme for the channel. Examples of modulation schemes aredifferential phase shift key (“DPSK”) modulation, 64-QAM modulation andOFDM. It should be noted here that the diversity gains provided by MIMOallow for higher-order modulation constellations that would otherwise befeasible in a SISO (Single Input-Single Output) system utilizing thesame channel. Each coded and modulated signal is then transmittedthrough its own antenna 104 following D/A conversion by a D/A conversionunit (not shown) and RF generation by each transmitter 103.

Assuming that adequate spatial diversity exists amongst the transmit andreceive antennas, each of the receiving antennas 105 will receive adifferent combination of the three transmitted signals from antennas104. Each signal is received and converted down to baseband by each RFreceiver 106, and digitized by an A/D converter (not shown). It y_(n) isthe signal received by the nth receive antenna 105, and x_(n) is thesignal transmitted by nth transmit antenna 104, and N is noise, this canbe described by the following three equations:y ₁ =x ₁ H ₁₁ +x ₂ H ₁₂ +x ₃ H ₁₃ +Ny ₂ =x ₁ H ₂₁ +x ₂ H ₂₂ +x ₃ H ₂₃ +Ny ₃ =x ₁ H ₃₁ +x ₂ H ₃₂ +x ₃ H ₃₃ +N

Given that this is a system of three equations with three unknowns, itis a matter of linear algebra for the signal processing subsystem 107 toderive x₁, x₂, and x₃ (assuming that N is at a low enough level topermit decoding of the signals):x ₁ =y ₁ H ⁻¹ ₁₁ +y ₂ H ⁻¹ ₁₂ +y ₃ H ⁻¹ ₁₃x ₂ =y ₁ H ⁻¹ ₂₁ +y ₂ H ⁻¹ ₂₂ +y ₃ H ⁻¹ ₂₃x ₃ =y ₁ H ⁻¹ ₃₁ +y ₂ H ⁻¹ ₃₂ +y ₃ H ⁻¹ ₃₃

Once the three transmitted signals x_(n) are thus derived, they are thendemodulated, decoded, and error-corrected by signal processing subsystem107 to recover the three bit streams that were originally separated outby splitter 101. These bit streams are combined in combiner unit 108,and output as a single data stream from the data output 109. Assumingthe robustness of the system is able to overcome the noise impairments,the data output 109 will produce the same bit stream that was introducedto the data Input 100.

Although the prior art system just described is generally practical upto four antennas, and perhaps up to as many as 10, for the reasonsdescribed in the Background section of this disclosure, it becomesimpractical with large numbers of antennas (e.g. 25, 100, or 1000).

Typically, such a prior art system is two-way, and the return path isimplemented exactly the same way, but in reverse, with each side of thecommunications channels having both transmit and receive subsystems.

FIG. 2 illustrates one embodiment of the invention in which a BaseStation (BS) 200 is configured with a Wide Area Network (WAN) interface(e.g. to the Internet through a T1 or other high speed connection) 201and is provisioned with a number (N) of antennas 202. For the timebeing, we use the term “Base Station” to refer to any wireless stationthat communicates wirelessly with a set of clients from a fixedlocation. Examples of Base Stations are access points in wireless localarea networks (WLANs) or WAN antenna tower or antenna array. There are anumber of Client Devices 203-207, each with a single antenna, which areserved wirelessly from the Base Station 200. Although for the purposesof this example it is easiest to think about such a Base Station asbeing located in an office environment where it is serving ClientDevices 203-207 that are wireless-network equipped personal computers,this architecture will apply to a large number of applications, bothindoor and outdoor, where a Base Station is serving wireless clients.For example, the Base Station could be based at a cellular phone tower,or on a television broadcast tower. In one embodiment, the Base Station200 is positioned on the ground and is configured to transmit upward atHF frequencies (e.g., frequencies up to 24 MHz) to bounce signals offthe ionosphere as described in co-pending application entitled SYSTEMAND METHOD FOR ENHANCING NEAR VERTICAL INCIDENCE SKYWAVE (“NVIS”)COMMUNICATION USING SPACE-TIME CODING, Ser. No. 10/817,731, Filed Apr.20, 2004, which is assigned to the assignee of the present applicationand which is incorporated herein by reference.

Certain details associated with the Base Station 200 and Client Devices203-207 set forth above are for the purpose of illustration only and arenot required for complying with the underlying principles of theinvention. For example, the Base Station may be connected to a varietyof different types of wide area networks via WAN interface 201 includingapplication-specific wide area networks such as those used for digitalvideo distribution. Similarly, the Client Devices may be any variety ofwireless data processing and/or communication devices including, but notlimited to cellular phones, personal digital assistants (“PDAs”),receivers, and wireless cameras.

In one embodiment, the Base Station's n Antennas 202 are separatedspatially such that each is transmitting and receiving signals which arenot spatially correlated, just as if the Base Station was a prior artMIMO transceiver. As described in the Background, experiments have beendone where antennas placed within λ/6 (i.e. ⅙ wavelength) apartsuccessfully achieve an increase in throughput from MIMO, but generallyspeaking, the further apart these Base Station antennas are placed, thebetter the system performance, and λ/2 is a desirable minimum. Ofcourse, the underlying principles of the invention are not limited toany particular separation between antennas.

Note that a single Base Station 200 may very well have its antennaslocated very far apart. For example, in the HF spectrum, the antennasmay be 10 meters apart or more (e.g., in an NVIS implementationmentioned above). If 100 such antennas are used, the Base Station'santenna array could well occupy several square kilometers.

In addition to spatial diversity techniques, one embodiment of theinvention polarizes the signal in order to increase the effectivethroughput of the system. Increasing channel capacity throughpolarization is a well known technique which has been employed bysatellite television providers for years. Using polarization, it ispossible to have multiple (e.g., three) Base Station or users' antennasvery close to each other, and still be not spatially correlated.Although conventional RF systems usually will only benefit from thediversity of two dimensions (e.g. x and y) of polarization, thearchitecture described herein may further benefit from the diversity ofthree dimensions of polarization (x, y and z).

In addition to space and polarization diversity, one embodiment of theinvention employs antennas with near-orthogonal radiation patterns toimprove link performance via pattern diversity. Pattern diversity canimprove the capacity and error-rate performance of MIMO systems and itsbenefits over other antenna diversity techniques have been shown in thefollowing papers:

-   [17] L. Dong, H. Ling, and R. W. Heath Jr., “Multiple-input    multiple-output wireless communication systems using antenna pattern    diversity,” Proc. IEEE Glob. Telecom. Conf., vol. 1, pp. 997-1001,    November 2002.-   [18] R. Vaughan, “Switched parasitic elements for antenna    diversity,” IEEE Trans. Antennas Propagat., vol. 47, pp. 399-405,    Feb. 1999.-   [19] P. Mattheijssen, M. H. A. J. Herben, G. Dolmans, and L. Leyten,    “Antenna-pattern diversity versus space diversity for use at    handhelds,” IEEE Trans. on Veh. Technol., vol. 53, pp. 1035-1042,    July 2004.-   [20] C. B. Dietrich Jr, K. Dietze, J. R. Nealy, and W. L. Stutzman,    “Spatial, polarization, and pattern diversity for wireless handheld    terminals,” Proc. IEEE Antennas and Prop. Symp., vol. 49, pp.    1271-1281, September 2001.-   [21] A. Forenza and R. W. Heath, Jr., “Benefit of Pattern Diversity    Via 2-element Array of Circular Patch Antennas in Indoor Clustered    MIMO Channels”, IEEE Trans. on Communications, vol. 54, no. 5, pp.    943-954, May 2006. Using pattern diversity, it is possible to have    multiple Base Station or users' antennas very close to each other,    and still be not spatially correlated.

FIG. 3 provides additional detail of one embodiment of the Base Station200 and Client Devices 203-207 shown in FIG. 2. For the purposes ofsimplicity, the Base Station 300 is shown with only three antennas 305and only three Client Devices 306-308. It will be noted, however, thatthe embodiments of the invention described herein may be implementedwith a virtually unlimited number of antennas 305 (i.e., limited only byavailable space and noise) and Client Devices 306-308.

FIG. 3 is similar to the prior art MIMO architecture shown in FIG. 1 inthat both have three antennas on each sides of a communication channel.A notable difference is that in the prior art MIMO system the threeantennas 105 on the right side of FIG. 1 are all a fixed distance fromone another (e.g., integrated on a single device), and the receivedsignals from each of the antennas 105 are processed together in theSignal Processing subsystem 107. By contrast, in FIG. 3, the threeantennas 309 on the right side of the diagram are each coupled to adifferent Client Device 306-308, each of which may be distributedanywhere within range of the Base Station 305. As such, the signal thateach Client Device receives is processed independently from the othertwo received signals in its Coding, Modulation, Signal Processingsubsystem 311. Thus, in contrast to a Multiple-Input (i.e. antennas 105)Multiple-Output (i.e. antennas 104) “MIMO” system, FIG. 3 illustrates aMultiple Input (i.e. antennas 305) Distributed Output (i.e. antennas305) system, referred to hereinafter as a “MIDO” system.

Note that this application uses different terminology than previousapplications, so as to better conform with academic and industrypractices. In previously cited co-pending application, SYSTEM AND METHODFOR ENHANCING NEAR VERTICAL INCIDENCE SKYWAVE (“NVIS”) COMMUNICATIONUSING SPACE-TIME CODING, Ser. No. 10/817,731, Filed Apr. 20, 2004, andapplication Ser. No. 10/902,978 filed Jul. 30, 2004 for which this isapplication is a continuation-in-part, the meaning of “Input” and“Output” (in the context of SIMO, MISO, DIMO and MIDO) is reversed fromhow the terms are used in this application. In the prior applications,“Input” referred to the wireless signals as they are input to thereceiving antennas (e.g. antennas 309 in FIG. 3), and “Output” referredto the wireless signals as they are output by the transmitting antennas(e.g. antennas 305). In academia and the wireless industry, the reversemeaning of “Input” and “Output” is commonly used, in which “Input”refers to the wireless signals as they are input to the channel (i.e.the transmitted wireless signals from antennas 305) and “Output” refersto the wireless signals as they are output from the channel (i.e.wireless signals received by antennas 309). This application adopts thisterminology, which is the reverse of the applications cited previouslyin this paragraph. Thus, the following terminology equivalences shall bedrawn between applications: 10/817,731 and 10/902,978 CurrentApplication SIMO = MISO MISO = SIMO DIMO = MIDO MIDO = DIMO

The MIDO architecture shown in FIG. 3 achieves a similar capacityincrease as MIMO over a SISO system for a given number of transmittingantennas. However, one difference between MIMO and the particular MIDOembodiment illustrated in FIG. 3 is that, to achieve the capacityincrease provided by multiple base station antennas, each MIDO ClientDevice 306-308 requires only a single receiving antenna, whereas withMIMO, each Client Device requires as least as many receiving antennas asthe capacity multiple that is hoped to be achieved. Given that there isusually a practical limit to how many antennas can be placed on a ClientDevice (as explained in the Background), this typically limits MIMOsystems to between four to ten antennas (and 4× to 10× capacitymultiple). Since the Base Station 300 is typically serving many ClientDevices from a fixed and powered location, is it practical to expand itto far more antennas than ten, and to separate the antennas by asuitable distance to achieve spatial diversity. As illustrated, eachantenna is equipped with a transceiver 304 and a portion of theprocessing power of a Coding, Modulation, and Signal Processing section303. Significantly, in this embodiment, no matter how much Base Station300 is expanded, each Client Device 306-308 only will require oneantenna 309, so the cost for an individual user Client Device 306-308will be low, and the cost of Base Station 300 can be shared among alarge base of users.

An example of how a MIDO transmission from the Base Station 300 to theClient Devices 306-308 can be accomplished is illustrated in FIGS. 4through 6.

In one embodiment of the invention, before a MIDO transmission begins,the channel is characterized. As with a MIMO system, a training signalis transmitted (in the embodiment herein described), one-by-one, by eachof the antennas 405. FIG. 4 illustrates only the first training signaltransmission, but with three antennas 405 there are three separatetransmissions in total. Each training signal is generated by the Coding,Modulation, and Signal Processing subsystem 403, converted to analogthrough a D/A converter, and transmitted as RF through each RFTransceiver 404. Various different coding, modulation and signalprocessing techniques may be employed including, but not limited to,those described above (e.g., Reed Solomon, Viterbi coding; QAM, DPSK,QPSK modulation, . . . etc).

Each Client Device 406-408 receives a training signal through itsantenna 409 and converts the training signal to baseband by Transceiver410. An A/D converter (not shown) converts the signal to digital whereis it processed by each Coding, Modulation, and Signal Processingsubsystem 411. Signal characterization logic 320 then characterizes theresulting signal (e.g., identifying phase and amplitude distortions asdescribed above) and stores the characterization in memory. Thischaracterization process is similar to that of prior art MIMO systems,with a notable difference being that the each client device onlycomputes the characterization vector for its one antenna, rather thanfor n antennas. For example, the Coding Modulation and Signal Processingsubsystem 420 of client device 406 is initialized with a known patternof the training signal (either at the time of manufacturing, byreceiving it in a transmitted message, or through another initializationprocess). When antenna 405 transmits the training signal with this knownpattern, Coding Modulation and Signal Processing subsystem 420 usescorrelation methods to find the strongest received pattern of thetraining signal, it stores the phase and amplitude offset, then itsubtracts this pattern from the received signal. Next, it finds thensecond strongest received pattern that correlates to the trainingsignal, it stores the phase and amplitude offset, then it subtracts thissecond strongest pattern from the received signal. This processcontinues until either some fixed number of phase and amplitude offsetsare stored (e.g. eight), or a detectable training signal pattern dropsbelow a given noise floor. This vector of phase/amplitude offsetsbecomes element H₁₁ of the vector 413. Simultaneously, Coding Modulationand Signal Processing subsystems for Client Devices 407 and 408implement the same processing to produce their vector elements H₂₁ andH₃₁.

The memory in which the characterization is stored may be a non-volatilememory such as a Flash memory or a hard drive and/or a volatile memorysuch as a random access memory (e.g., SDRAM, RDAM). Moreover, differentClient Devices may concurrently employ different types of memories tostore the characterization information (e.g., PDA's may use Flash memorywhereas notebook computers may use a hard drive). The underlyingprinciples of the invention are not limited to any particular type ofstorage mechanism on the various Client Devices or the Base Station.

As mentioned above, depending on the scheme employed, since each ClientDevice 406-408 has only one antenna, each only stores a 1×3 row 413-415of the H matrix. FIG. 4 illustrates the stage after the first trainingsignal transmission where the first column of 1×3 rows 413-415 has beenstored with channel characterization information for the first of thethree Base Station antennas 405. The remaining two columns are storedfollowing the channel characterization of the next two training signaltransmissions from the remaining two base station antennas. Note thatfor the sake of illustration the three training signals are transmittedat separate times. If the three training signal patterns are chosen suchas not to be correlated to one another, they may be transmittedsimultaneously, thereby reducing training time.

As indicated in FIG. 5, after all three pilot transmissions arecomplete, each Client Device 506-508 transmits back to the Base Station500 the 1×3 row 513-515 of matrix H that it has stored. To the sake ofsimplicity, only one Client Device 506 is illustrated transmitting itscharacterization information in FIG. 5. An appropriate modulation scheme(e.g. DPSK, 64QAM, OFDM) for the channel combined with adequate errorcorrection coding (e.g. Reed Solomon, Viterbi, and/or Turbo codes) maybe employed to make sure that the Base Station 500 receives the data inthe rows 513-515 accurately.

Although all three antennas 505 are shown receiving the signal in FIG.5, it is sufficient for a single antenna and transceiver of the BaseStation 500 to receive each 1×3 row 513-515 transmission. However,utilizing many or all of antennas 505 and Transceivers 504 to receiveeach transmission (i.e., utilizing prior art Single-InputMultiple-Output (“SIMO”) processing techniques in the Coding, Modulationand Signal Processing subsystem 503) may yield a better signal-to-noiseratio (“SNR”) than utilizing a single antenna 505 and Transceiver 504under certain conditions.

As the Coding, Modulation and Signal Processing subsystem 503 of BaseStation 500 receives the 1×3 row 513-515, from each Client Device507-508, it stores it in a 3×3H matrix 516. As with the Client Devices,the Base Station may employ various different storage technologiesincluding, but not limited to non-volatile mass storage memories (e.g.,hard drives) and/or volatile memories (e.g., SDRAM) to store the matrix516. FIG. 5 illustrates a stage at which the Base Station 500 hasreceived and stored the 1×3 row 513 from Client Device 509. The 1×3 rows514 and 515 may be transmitted and stored in H matrix 516 as they arereceived from the remaining Client Devices, until the entire H matrix516 is stored.

One embodiment of a MIDO transmission from a Base Station 600 to ClientDevices 606-608 will now be described with reference to FIG. 6. Becauseeach Client Device 606-608 is an independent device, typically eachdevice is receiving a different data transmission. As such, oneembodiment of a Base Station 600 includes a Router 602 communicativelypositioned between the WAN Interface 601 and the Coding, Modulation andSignal Processing subsystem 603 that sources multiple data streams(formatted into bit streams) from the WAN interface 601 and routes themas separate bit streams u₁-u₃ intended for each Client Device 606-608,respectively. Various well known routing techniques may be employed bythe router 602 for this purpose.

The three bit streams, u₁-u₃, shown in FIG. 6 are then routed into theCoding, Modulation and Signal Processing subsystem 603 and coded intostatistically distinct, error correcting streams (e.g. using ReedSolomon, Viterbi, or Turbo Codes) and modulated using an appropriatemodulation scheme for the channel (such as DPSK, 64QAM or OFDM). Inaddition, the embodiment illustrated in FIG. 6 includes signal precedinglogic 630 for uniquely coding the signals transmitted from each of theantennas 605 based on the signal characterization matrix 616. Morespecifically, rather than routing each of the three coded and modulatedbit streams to a separate antenna (as is done in FIG. 1), in oneembodiment, the preceding logic 630 multiplies the three bit streamsu₁-u₃ in FIG. 6 by the inverse of the H matrix 616, producing three newbit streams, u′₁-u′₃. The three precoded bit streams are then convertedto analog by D/A converters (not shown) and transmitted as RF byTransceivers 604 and antennas 605.

Before explaining how the bit streams are received by the Client Devices606-608, the operations performed by the preceding module 630 will bedescribed. Similar to the MIMO example from FIG. 1 above, the coded andmodulated signal for each of the three source bit streams will bedesignated with u_(n). In the embodiment illustrated in FIG. 6, eachu_(i) contains the data from one of the three bit streams routed by theRouter 602, and each such bit stream is intended for one of the threeClient Devices 606-608.

However, unlike the MIMO example of FIG. 1, where each x_(i) istransmitted by each antenna 104, in the embodiment of the inventionillustrated in FIG. 6, each u_(i) is received at each Client Deviceantenna 609 (plus whatever noise N there is in the channel). To achievethis result, the output of each of the three antennas 605 (each of whichwe will designate as v_(i)) is a function of u_(i) and the H matrix thatcharacterizes the channel for each Client Device. In one embodiment,each v_(i) is calculated by the precoding logic 630 within the Coding,Modulation and Signal Processing subsystem 603 by implementing thefollowing formulas:v ₁ =u ₁ H ⁻¹ ₁₁ +u ₂ H ⁻¹ ₁₂ +u ₃ H ⁻¹ ₁₃v ₂ =u ₁ H ⁻¹ ₂₁ +u ₂ H ⁻¹ ₂₂ +u ₃ H ⁻¹ ₂₃v ₃ =u ₁ H ⁻¹ ₃₁ +u ₂ H ⁻¹ ₃₂ +u ₃ H ⁻¹ ₃₃

Thus, unlike MIMO, where each x_(i) is calculated at the receiver afterthe signals have been transformed by the channel, the embodiments of theinvention described herein solve for each v_(i) at the transmitterbefore the signals have been transformed by the channel. Each antenna609 receives u_(i) already separated from the other u_(n-1) bit streamsintended for the other antennas 609. Each Transceiver 610 converts eachreceived signal to baseband, where it is digitized by an A/D converter(now shown), and each Coding, Modulation and Signal Processing subsystem611, demodulates and decodes the x_(i) bit stream intended for it, andsends its bit stream to a Data Interface 612 to be used by the ClientDevice (e.g., by an application on the client device).

The embodiments of the invention described herein may be implementedusing a variety of different coding and modulation schemes. For example,in an OFDM implementation, where the frequency spectrum is separatedinto a plurality of sub-bands, the techniques described herein may beemployed to characterize each individual sub-band. As mentioned above,however, the underlying principles of the invention are not limited toany particular modulation scheme.

If the Client Devices 606-608 are portable data processing devices suchas PDAs, notebook computers, and/or wireless telephones the channelcharacterization may change frequently as the Client Devices may movefrom one location to another. As such, in one embodiment of theinvention, the channel characterization matrix 616 at the Base Stationis continually updated. In one embodiment, the Base Station 600periodically (e.g., every 250 milliseconds) sends out a new trainingsignal to each Client Device, and each Client Device continuallytransmits its channel characterization vector back to the Base Station600 to ensure that the channel characterization remains accurate (e.g.if the environment changes so as to affect the channel or if a ClientDevice moves). In one embodiment, the training signal is interleavedwithin the actual data signal sent to each client device. Typically, thetraining signals are much lower throughput than the data signals, sothis would have little impact on the overall throughput of the system.Accordingly, in this embodiment, the channel characterization matrix 616may be updated continuously as the Base Station actively communicateswith each Client Device, thereby maintaining an accurate channelcharacterization as the Client Devices move from one location to thenext or if the environment changes so as to affect the channel.

One embodiment of the invention illustrated in FIG. 7 employs MIMOtechniques to improve the upstream communication channel (i.e., thechannel from the Client Devices 706-708 to the Base Station 700). Inthis embodiment, the channel from each of the Client Devices iscontinually analyzed and characterized by upstream channelcharacterization logic 741 within the Base Station. More specifically,each of the Client Devices 706-708 transmits a training signal to theBase Station 700 which the channel characterization logic 741 analyzes(e.g., as in a typical MIMO system) to generate an N×M channelcharacterization matrix 741, where N is the number of Client Devices andM is the number of antennas employed by the Base Station. The embodimentillustrated in FIG. 7 employs three antennas 705 at the Base Station andthree Client Devices 706-608, resulting in a 3×3 channelcharacterization matrix 741 stored at the Base Station 700. The MIMOupstream transmission illustrated in FIG. 7 may be used by the ClientDevices both for transmitting data back to the Base Station 700, and fortransmitting channel characterization vectors back to the Base Station700 as illustrated in FIG. 5. But unlike the embodiment illustrated inFIG. 5 in which each Client Device's channel characterization vector istransmitted at a separate time, the method shown in FIG. 7 allows forthe simultaneous transmission of channel characterization vectors frommultiple Client Devices back to the Base Station 700, therebydramatically reducing the channel characterization vectors' impact onreturn channel throughput.

As mentioned above, each signal's characterization may include manyfactors including, for example, phase and amplitude relative to areference internal to the receiver, an absolute reference, a relativereference, characteristic noise, or other factors. For example, in aquadrature amplitude modulation (“QAM”)-modulated signal thecharacterization might be a vector of the phase and amplitude offsets ofseveral multipath images of the signal. As another example, in anorthogonal frequency division multiplexing (“OFDM”)-modulated signal, itmight be a vector of the phase and amplitude offsets of several or allof the individual sub-signals in the OFDM spectrum. The training signalmay be generated by each Client Device's coding and modulation subsystem711, converted to analog by a D/A converter (not shown), and thenconverted from baseband to RF by each Client Device's transmitter 709.In one embodiment, in order to ensure that the training signals aresynchronized, Client Devices only transmit training signals whenrequested by the Base Station (e.g., in a round robin manner). Inaddition, training signals may be interleaved within or transmittedconcurrently with the actual data signal sent from each client device.Thus, even if the Client Devices 706-708 are mobile, the trainingsignals may be continuously transmitted and analyzed by the upstreamchannel characterization logic 741, thereby ensuring that the channelcharacterization matrix 741 remains up-to-date.

The total channel capacity supported by the foregoing embodiments of theinvention may be defined as min (N, M) where M is the number of ClientDevices and N is the number of Base Station antennas. That is, thecapacity is limited by the number of antennas on either the Base Stationside or the Client side. As such, one embodiment of the inventionemploys synchronization techniques to ensure that no more than min (N,M) antennas are transmitting/receiving at a given time.

In a typical scenario, the number of antennas 705 on the Base Station700 will be less than the number of Client Devices 706-708. An exemplaryscenario is illustrated in FIG. 8 which shows five Client Devices804-808 communicating with a base station having three antennas 802. Inthis embodiment, after determining the total number of Client Devices804-808, and collecting the necessary channel characterizationinformation (e.g., as described above), the Base Station 800 chooses afirst group of three clients 810 with which to communicate (threeclients in the example because min (N, M)=3). After communicating withthe first group of clients 810 for a designated period of time, the BaseStation then selects another group of three clients 811 with which tocommunicate. To distribute the communication channel evenly, the BaseStation 800 selects the two Client Devices 807, 808 which were notincluded in the first group. In addition, because an extra antenna isavailable, the Base Station 800 selects an additional client device 806included in the first group. In one embodiment, the Base Station 800cycles between groups of clients in this manner such that each client iseffectively allocated the same amount of throughput over time. Forexample, to allocate throughput evenly, the Base Station maysubsequently select any combination of three Client Devices whichexcludes Client Device 806 (i.e., because Client Device 806 was engagedin communication with the Base Station for the first two cycles).

In one embodiment, in addition to standard data communications, the BaseStation may employ the foregoing techniques to transmit training signalsto each of the Client Devices and receive training signals and signalcharacterization data from each of the Client Devices.

In one embodiment, certain Client Devices or groups of client devicesmay be allocated different levels of throughput. For example, ClientDevices may be prioritized such that relatively higher priority ClientDevices may be guaranteed more communication cycles (i.e., morethroughput) than relatively lower priority client devices. The“priority” of a Client Device may be selected based on a number ofvariables including, for example, the designated level of a user'ssubscription to the wireless service (e.g., user's may be willing to paymore for additional throughput) and/or the type of data beingcommunicated to/from the Client Device (e.g., real-time communicationsuch as telephony audio and video may take priority over non-real timecommunication such as email).

In one embodiment of the Base Station dynamically allocates throughputbased on the Current Load required by each Client Device. For example,if Client Device 804 is streaming live video and the other devices805-808 are performing non-real time functions such as email, then theBase Station 800 may allocate relatively more throughput to this client804. It should be noted, however, that the underlying principles of theinvention are not limited to any particular throughput allocationtechnique.

As illustrated in FIG. 9, two Client Devices 907, 908 may be so close inproximity, that the channel characterization for the clients iseffectively the same. As a result, the Base Station will receive andstore effectively equivalent channel characterization vectors for thetwo Client Devices 907, 908 and therefore will not be able to createunique, spatially distributed signals for each Client Device.Accordingly, in one embodiment, the Base Station will ensure that anytwo or more Client Devices which are in close proximity to one anotherare allocated to different groups. In FIG. 9, for example, the BaseStation 900 first communicates with a first group 910 of Client Devices904, 905 and 908; and then with a second group 911 of Client Devices905, 906, 907, ensuring that Client Devices 907 and 908 are in differentgroups.

Alternatively, in one embodiment, the Base Station 900 communicates withboth Client Devices 907 and 908 concurrently, but multiplexes thecommunication channel using known channel multiplexing techniques. Forexample, the Base Station may employ time division multiplexing (“TDM”),frequency division multiplexing (“FDM”) or code division multiple access(“CDMA”) techniques to divide the single, spatially-correlated signalbetween Client Devices 907 and 908.

Although each Client Device described above is equipped with a singleantenna, the underlying principles of the invention may be employedusing Client Devices with multiple antennas to increase throughput. Forexample, when used on the wireless systems described above, a clientwith 2 antennas will realize a 2× increase in throughput, a client with3 antennas will realize a 3× increase in throughput, and so on (i.e.,assuming that the spatial and angular separation between the antennas issufficient). The Base Station may apply the same general rules whencycling through Client Devices with multiple antennas. For example, itmay treat each antenna as a separate client and allocate throughput tothat “client” as it would any other client (e.g., ensuring that eachclient is provided with an adequate or equivalent period ofcommunication).

As mentioned above, one embodiment of the invention employs the MIDOand/or MIMO signal transmission techniques described above to increasethe signal-to-noise ratio and throughput within a Near VerticalIncidence Skywave (“NVIS”) system. Referring to FIG. 10, in oneembodiment of the invention, a first NVIS station 1001 equipped with amatrix of N antennas 1002 is configured to communicate with M clientdevices 1004. The NVIS antennas 1002 and antennas of the various clientdevices 1004 transmit signals upward to within about 15 degrees ofvertical in order to achieve the desired NVIS and minimize ground waveinterference effects. In one embodiment, the antennas 1002 and clientdevices 1004, support multiple independent data streams 1006 using thevarious MIDO and MIMO techniques described above at a designatedfrequency within the NVIS spectrum (e.g., at a carrier frequency at orbelow 23 MHz, but typically below 10 MHz), thereby significantlyincreasing the throughput at the designated frequency (i.e., by a factorproportional to the number of statistically independent data streams).

The NVIS antennas serving a given station may be physically very farapart from each other. Given the long wavelengths below 10 MHz and thelong distance traveled for the signals (as much as 300 miles roundtrip), physical separation of the antennas by 100 s of yards, and evenmiles, can provide advantages in diversity. In such situations, theindividual antenna signals may be brought back to a centralized locationto be processed using conventional wired or wireless communicationssystems. Alternatively, each antenna can have a local facility toprocess its signals, then use conventional wired or wirelesscommunications systems to communicate the data back to a centralizedlocation. In one embodiment of the invention, NVIS Station 1001 has abroadband link 1015 to the Internet 1010 (or other wide area network),thereby providing the client devices 1003 with remote, high speed,wireless network access.

In one embodiment, the Base Station and/or users may exploitpolarization/pattern diversity techniques described above to reduce thearray size and/or users' distance while providing diversity andincreased throughput. As an example, in MIDO systems with HFtransmissions, the users may be in the same location and yet theirsignals be uncorrelated because of polarization/pattern diversity. Inparticular, by using pattern diversity, one user may be communicating tothe Base Station via groundwave whereas the other user via NVIS.

Additional Embodiments of the Invention

I. DIDO-OFDM Precoding with I/Q Imbalance

One embodiment of the invention employs a system and method tocompensate for in-phase and quadrature (I/Q) imbalance indistributed-input distributed-output (DIDO) systems with orthogonalfrequency division multiplexing (OFDM). Briefly, according to thisembodiment, user devices estimate the channel and feedback thisinformation to the Base Station; the Base Station computes the precedingmatrix to cancel inter-carrier and inter-user interference caused by I/Qimbalance; and parallel data streams are transmitted to multiple userdevices via DIDO precoding; the user devices demodulate data viazero-forcing (ZF), minimum mean-square error (MMSE) or maximumlikelihood (ML) receiver to suppress residual interference.

As described in detail below, some of the significant features of thisembodiment of the invention include, but are not limited to:

Precoding to cancel inter-carrier interference (ICI) from mirror tones(due to I/Q mismatch) in OFDM systems;

Precoding to cancel inter-user interference and ICI (due to I/Qmismatch) in DIDO-OFDM systems;

Techniques to cancel ICI (due to I/Q mismatch) via ZF receiver inDIDO-OFDM systems employing block diagonalization (BD) precoder;

Techniques to cancel inter-user interference and ICI (due to I/Qmismatch) via precoding (at the transmitter) and a ZF or MMSE filter (atthe receiver) in DIDO-OFDM systems;

Techniques to cancel inter-user interference and ICI (due to I/Qmismatch) via pre-coding (at the transmitter) and a nonlinear detectorlike a maximum likelihood (ML) detector (at the receiver) in DIDO-OFDMsystems;

The use of pre-coding based on channel state information to cancelinter-carrier interference (ICI) from mirror tones (due to I/Q mismatch)in OFDM systems;

The use of pre-coding based on channel state information to cancelinter-carrier interference (ICI) from mirror tones (due to I/Q mismatch)in DIDO-OFDM systems;

The use of an I/Q mismatch aware DIDO precoder at the station and anIQ-aware DIDO receiver at the user terminal;

The use of an I/Q mismatch aware DIDO precoder at the station, an I/Qaware DIDO receiver at the user terminal, and an I/Q aware channelestimator;

The use of an I/Q mismatch aware DIDO precoder at the station, an I/Qaware DIDO receiver at the user terminal, an I/Q aware channelestimator, and an I/Q aware DIDO feedback generator that sends channelstate information from the user terminal to the station;

The use of an I/Q mismatch-aware DIDO precoder at the station and an I/Qaware DIDO configurator that uses I/Q channel information to performfunctions including user selection, adaptive coding and modulation,space-time-frequency mapping, or precoder selection;

The use of an I/Q aware DIDO receiver that cancels ICI (due to I/Qmismatch) via ZF receiver in DIDO-OFDM systems employing blockdiagonalization (BD) precoder;

The use of an I/Q aware DIDO receiver that cancels ICI (due to I/Qmismatch) via pre-coding (at the transmitter) and a nonlinear detectorlike a maximum likelihood detector (at the receiver) in DIDO-OFDMsystems; and

The use of an I/Q aware DIDO receiver that cancels ICI (due to I/Qmismatch) via ZF or MMSE filter in DIDO-OFDM systems.

a. Background

The transmit and receive signals of typical wireless communicationsystems consist of in-phase and quadrature (I/Q) components. Inpractical systems, the inphase and quadrature components may bedistorted due to imperfections in the mixing and baseband operations.These distortions manifest as I/Q phase, gain and delay mismatch. Phaseimbalance is caused by the sine and cosine in the modulator/demodulatornot being perfectly orthogonal. Gain imbalance is caused by differentamplifications between the inphase and quadrature components. There maybe an additional distortion, called delay imbalance, due to differencein delays between the I- and Q-rails in the analog circuitry.

In orthogonal frequency division multiplexing (OFDM) systems, I/Qimbalance causes inter-carrier interference (ICI) from the mirror tones.This effect has been studied in the literature and methods to compensatefor I/Q mismatch in single-input single-output SISO-OFDM systems havebeen proposed in M. D. Benedetto and P. Mandarini, “Analysis of thee□ect of the I/Q baseband filter mismatch in an OFDM modem,” Wirelesspersonal communications, pp. 175-186, 2000; S. Schuchert and R.Hasholzner, “A novel I/Q imbalance compensation scheme for the receptionof OFDM signals,” IEEE Transaction on Consumer Electronics, August 2001;M. Valkama, M. Renfors, and V. Koivunen, “Advanced methods for I/Qimbalance compensation in communication receivers,” IEEE Trans. Sig.Proc., October 2001; R. Rao and B. Daneshrad, “Analysis of I/Q mismatchand a cancellation scheme for OFDM systems,” IST Mobile CommunicationSummit, June 2004; A. Tarighat, R. Bagheri, and A. H. Sayed,“Compensation schemes and performance analysis of IQ imbalances in OFDMreceivers,” Signal Processing, IEEE Transactions on [see also Acoustics,Speech, and Signal Processing, IEEE Transactions on], vol. 53, pp.3257-3268, August 2005.

An extension of this work to multiple-input multiple-output MIMO-OFDMsystems was presented in R. Rao and B. Daneshrad, “I/Q mismatchcancellation for MIMO OFDM systems,” in Personal, Indoor and MobileRadio Communications, 2004; PIMRC 2004.15th IEEE International Symposiumon, vol. 4, 2004, pp. 2710-2714. R. M. Rao, W. Zhu, S. Lang, C. Oberli,D. Browne, J. Bhatia, J. F. Frigon, J. Wang, P; Gupta, H. Lee, D. N.Liu, S. G. Wong, M. Fitz, B. Daneshrad, and O. Takeshita, “Multiantennatestbeds for research and education in wireless communications,” IEEECommunications Magazine, vol. 42, no. 12, pp. 72-81, December 2004; S.Lang, M. R. Rao, and B. Daneshrad, “Design and development of a 5.25 GHzsoftware defined wireless OFDM communication platform,” IEEECommunications Magazine, vol. 42, no. 6, pp. 6-12, June 2004, forspatial multiplexing (SM) and in A. Tarighat and A. H. Sayed, “MIMO OFDMreceivers for systems with IQ imbalances,” IEEE Trans. Sig. Proc., vol.53, pp. 3583-3596, September 2005, for orthogonal space-time block codes(OSTBC).

Unfortunately, there is currently no literature on how to correct forI/Q gain and phase imbalance errors in a distributed-inputdistributed-output (DIDO) communication system. The embodiments of theinvention described below provide a solution to these problems.

DIDO systems consist of one Base Station with distributed antennas thattransmits parallel data streams (via pre-coding) to multiple users toenhance downlink throughput, while exploiting the same wirelessresources (i.e., same slot duration and frequency band) as conventionalSISO systems. A detailed description of DIDO systems was presented in S.G. Perlman and T. Cotter, “System and Method for DistributedInput-Distributed Output Wireless Communications,” Ser. No. 10/902,978,filed Jul. 30, 2004 (“Prior Application”), which is assigned to theassignee of the present application and which is incorporated herein byreference.

There are many ways to implement DIDO precoders. One solution is blockdiagonalization (BD) described in Q. H. Spencer, A. L. Swindlehurst, andM. Haardt, “Zero forcing methods for downlink spatial multiplexing inmultiuser MIMO channels,” IEEE Trans. Sig. Proc., vol. 52, pp. 461-471,February 2004. K. K. Wong, R. D. Murch, and K. B. Letaief, “A jointchannel diagonalization for multiuser MIMO antenna systems,” IEEE Trans.Wireless Comm., vol. 2, pp. 773-786, July 2003; L. U. Choi and R. D.Murch, “A transmit preprocessing technique for multiuser MIMO systemsusing a decomposition approach,” IEEE Trans. Wireless Comm., vol. 3, pp.20-24, January 2004; Z. Shen, J. G. Andrews, R. W. Heath, and B. L.Evans, “Low complexity user selection algorithms for multiuser MIMOsystems with block diagonalization,” accepted for publication in IEEETrans. Sig. Proc., September 2005; Z. Shen, R. Chen, J. G. Andrews, R.W. Heath, and B. L. Evans, “Sum capacity of multiuser MIMO broadcastchannels with block diagonalization,” submitted to IEEE Trans. WirelessComm., October 2005; R. Chen, R. W. Heath, and J. G. Andrews, “Transmitselection diversity for unitary precoded multiuser spatial multiplexingsystems with linear receivers,” accepted to IEEE Trans. on SignalProcessing, 2005. The methods for I/Q compensation presented in thisdocument assume BD precoder, but can be extended to any type of DIDOprecoder.

In DIDO-OFDM systems, I/Q mismatch causes two effects: ICI andinter-user interference. The former is due to interference from themirror tones as in SISO-OFDM systems. The latter is due to the fact thatI/Q mismatch destroys the orthogonality of the DIDO precoder yieldinginterference across users. Both of these types of interference can becancelled at the transmitter and receiver through the methods describedherein. Three methods for I/Q compensation in DIDO-OFDM systems aredescribed and their performance is compared against systems with andwithout I/Q mismatch. Results are presented based both on simulationsand practical measurements carried out with the DIDO-OFDM prototype.

The present embodiments are an extension of the Prior Application. Inparticular, these embodiments relate to the following features of thePrior Application:

The system as described in the prior application, where the I/Q railsare affected by gain and phase imbalance;

The training signals employed for channel estimation are used tocalculate the DIDO precoder with I/Q compensation at the transmitter;and

The signal characterization data accounts for distortion due to I/Qimbalance and is used at the transmitter to compute the DIDO precoderaccording to the method proposed in this document.

b. Embodiments of the Invention

First, the mathematical model and framework of the invention will bedescribed.

Before presenting the solution, it is useful to explain the coremathematical concept. We explain it assuming I/Q gain and phaseimbalance (phase delay is not included in the description but is dealtwith automatically in the DIDO-OFDM version of the algorithm). Toexplain the basic idea, suppose that we want to multiply two complexnumbers s=s_(I)+js_(Q) and h=h_(I)+jh_(Q) and let x=h*s. We use thesubscripts to denote inphase and quadrature components. Recall thatx _(I) =s _(I) h _(I) −S _(Q) h _(Q)andx _(Q) =s _(I) h _(Q) +s _(Q) h _(I).

In matrix form this can be rewritten as $\begin{bmatrix}x_{I} \\x_{Q}\end{bmatrix} = {{\begin{bmatrix}h_{I} & {- h_{Q}} \\h_{Q} & h_{I}\end{bmatrix}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}}.}$

Note the unitary transformation by the channel matrix (H). Now supposethat s is the transmitted symbol and h is the channel. The presence ofI/Q gain and phase imbalance can be modeled by creating a non-unitarytransformation as follows $\begin{matrix}{\begin{bmatrix}x_{I} \\x_{Q}\end{bmatrix} = {{\begin{bmatrix}h_{11} & h_{12} \\h_{21} & h_{22}\end{bmatrix}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}}.}} & (A)\end{matrix}$

The trick is to recognize that it is possible to write $\begin{matrix}{\begin{bmatrix}h_{11} & h_{12} \\h_{21} & h_{22}\end{bmatrix} = {{\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}} +}} \\{\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {h_{12} + h_{21}} \\{{- h_{12}} + h_{21}} & {h_{22} - h_{11}}\end{bmatrix}} \\{= {{\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}} +}} \\{\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {- \left( {h_{12} + h_{21}} \right)} \\{h_{12} + h_{21}} & {h_{11} - h_{22}}\end{bmatrix}} \\{\begin{bmatrix}1 & 0 \\0 & {- 1}\end{bmatrix}.}\end{matrix}$

Now, rewriting (A) $\begin{matrix}\begin{matrix}{\begin{bmatrix}x_{I} \\x_{Q}\end{bmatrix} = {{{\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}} +}} \\{{{\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {- \left( {h_{12} + h_{21}} \right)} \\{h_{12} + h_{21}} & {h_{11} - h_{22}}\end{bmatrix}}\begin{bmatrix}1 & 0 \\0 & {- 1}\end{bmatrix}}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}} \\{= {{{\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}} +}} \\{{\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {- \left( {h_{12} + h_{21}} \right)} \\{h_{12} + h_{21}} & {h_{11} - h_{22}}\end{bmatrix}}\begin{bmatrix}s_{I} \\{- s_{Q}}\end{bmatrix}}\end{matrix} & (5)\end{matrix}$

Let us define $\mathcal{H}_{e} = {\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}}$ and $\mathcal{H}_{c} = {{\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {- \left( {h_{12} + h_{21}} \right)} \\{h_{12} + h_{21}} & {h_{11} - h_{22}}\end{bmatrix}}.}$

Both of these matrices have a unitary structure thus can be equivalentlyrepresented by complex scalars ash _(e) =h ₁₁ +h ₂₂ +j(h ₂₁ −h ₁₂)andh _(c) =h ₁₁ −h ₂₂ +j(h ₂₁ +h ₁₂).

Using all of these observations, we can put the effective equation backin a scalar form with two channels: the equivalent channel h_(e) and theconjugate channel h_(c). Then the effective transformation in (5)becomesx=h _(e) s+h _(c) s*.

We refer to the first channel as the equivalent channel and the secondchannel as the conjugate channel. The equivalent channel is the one youwould observe if there were no I/Q gain and phase imbalance.

Using similar arguments, it can be shown that the input-outputrelationship of a discrete-time MIMO N×M system with I/Q gain and phaseimbalance is (using the scalar equivalents to build their matrixcounterparts)${x\lbrack t\rbrack} = {{\sum\limits_{\ell = 0}^{L}\quad{{h_{e}\lbrack\ell\rbrack}{s\left\lbrack {t - \ell} \right\rbrack}}} + {{h_{c}\lbrack\ell\rbrack}{s^{*}\left\lbrack {t - \ell} \right\rbrack}}}$

where t is the discrete time index, h_(e), h_(c)εC^(M×N), s=[s₁, . . . ,s_(N)], x=[x₁, . . . , x_(M)] and L is the number of channel taps.

In DIDO-OFDM systems, the received signal in the frequency domain isrepresented. Recall from signals and systems that ifFFT_(K){s[t]}=S[k] then FFT _(K) {s*[t]}=S*[(−k)]=S*[K−k] for k=0, 1, .. . , K−1.

With OFDM, the equivalent input-output relationship for a MIMO-OFDMsystem for subcarrier k isx[k]=H _(e) [k] s[k]+H _(c) [k] s*[K−k]  (1)

where k=0, 1, . . . , K−1 is the OFDM subcarrier index, H_(e) and H_(c)denote the equivalent and conjugate channel matrices, respectively,defined as${H_{e}\lbrack k\rbrack} = {\sum\limits_{\ell = 0}^{L}\quad{{h_{e}\lbrack\ell\rbrack}{\mathbb{e}}^{{- j}\frac{2{\prod k}}{K}\ell}}}$and${H_{c}\lbrack k\rbrack} = {\sum\limits_{\ell = 0}^{L}\quad{{h_{c}\lbrack\ell\rbrack}{{\mathbb{e}}^{{- j}\frac{2{\prod k}}{K}\ell}.}}}$

The second contribution in (1) is interference from the mirror tone. Itcan be dealt with by constructing the following stacked matrix system(note carefully the conjugates) $\begin{bmatrix}{\overset{\_}{x}\lbrack k\rbrack} \\{{\overset{\_}{x}}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix} = {\begin{bmatrix}{H_{e}\lbrack k\rbrack} & {H_{c}\lbrack k\rbrack} \\{H_{c}^{*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}\begin{bmatrix}{\overset{\_}{s}\lbrack k\rbrack} \\{{\overset{\_}{s}}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}}$

where s=[ s ₁, s ₂]^(T) and x=[ x ₁, x ₂]^(T) are the vectors oftransmit and receive symbols in the frequency domain, respectively.

Using this approach, an effective matrix is built to use for DIDOoperation. For example, with DIDO 2×2 the input-output relationship(assuming each user has a single receive antenna) the first user devicesees (in the absence of noise) $\begin{matrix}{\begin{bmatrix}{{\overset{\_}{x}}_{1}\lbrack k\rbrack} \\{{\overset{\_}{x}}_{1}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix} = {\begin{bmatrix}{H_{e}^{(1)}\lbrack k\rbrack} & {H_{c}^{(1)}\lbrack k\rbrack} \\{H_{c}^{{(1)}*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{{(1)}*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}{W\begin{bmatrix}{{\overset{\_}{s}}_{1}\lbrack k\rbrack} \\{{\overset{\_}{s}}_{1}^{*}\left\lbrack {K - k} \right\rbrack} \\{{\overset{\_}{s}}_{2}\lbrack k\rbrack} \\{{\overset{\_}{s}}_{2}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}}}} & (2)\end{matrix}$

while the second user observes $\begin{matrix}{\begin{bmatrix}{{\overset{\_}{x}}_{2}\lbrack k\rbrack} \\{{\overset{\_}{x}}_{2}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix} = {\begin{bmatrix}{H_{e}^{(2)}\lbrack k\rbrack} & {H_{c}^{(2)}\lbrack k\rbrack} \\{H_{c}^{{(2)}*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{{(2)}*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}{W\begin{bmatrix}{{\overset{\_}{s}}_{1}\lbrack k\rbrack} \\{{\overset{\_}{s}}_{1}^{*}\left\lbrack {K - k} \right\rbrack} \\{{\overset{\_}{s}}_{2}\lbrack k\rbrack} \\{{\overset{\_}{s}}_{2}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}}}} & (3)\end{matrix}$where H_(e) ^((m)), H_(c) ^((m))εC^(1×2) denote the m-th row of thematrices H_(e) and H_(c), respectively, and WεC^(4×4) is the DIDOpre-coding matrix. From (2) and (3) it is observed that the receivedsymbol x _(m)[k] of user m is affected by two sources of interferencecaused by I/Q imbalance: inter-carrier interference from the mirror tone(i.e., s*_(m)[K−k]) and inter-user interference (i.e., s _(p)[k] ands*_(p)[K−k] with p≠m). The DIDO precoding matrix W in (3) is designed tocancel these two interference terms.

There are several different embodiments of the DIDO precoder that can beused here depending on joint detection applied at the receiver. In oneembodiment, block diagonalization (BD) is employed (see, e.g., Q. H.Spencer, A. L. Swindlehurst, and M. Haardt, “Zeroforcing methods fordownlink spatial multiplexing in multiuser MIMO channels,” IEEE Trans.Sig. Proc., vol. 52, pp. 461-471, February 2004. K. K. Wong, R. D.Murch, and K. B. Letaief, “A joint-channel diagonalization for multiuserMIMO antenna systems,” IEEE Trans. Wireless Comm., vol. 2, pp. 773-786,July 2003. L. U. Choi and R. D. Murch, “A transmit preprocessingtechnique for multiuser MIMO systems using a decomposition approach,”IEEE Trans. Wireless Comm., vol. 3, pp. 20-24, January 2004. Z. Shen, J.G. Andrews, R. W. Heath, and B. L. Evans, “Low complexity user selectionalgorithms for multiuser MIMO systems with block diagonalization,”accepted for publication in IEEE Trans. Sig. Proc., September 2005. Z.Shen, R. Chen, J. G. Andrews, R. W. Heath, and B. L. Evans, “Sumcapacity of multiuser MIMO broadcast channels with blockdiagonalization,” submitted to IEEE Trans. Wireless Comm., October 2005,computed from the composite channel └H_(e) ^((m)), H_(c) ^((m))┘ (ratherthan H_(e) ^((m))). So, the current DIDO system chooses the precodersuch that w ⁢ = Δ ⁢ [ H e ( 1 ) ⁡ [ k ] H c ( 1 ) ⁡ [ k ] H c ( 1 ) * ⁡ [ K -k ] H e ( 1 ) * ⁡ [ K - k ] H e ( 2 ) ⁡ [ k ] H c ( 2 ) ⁡ [ k ] H c ( 2 ) *⁡[ K - k ] H e ( 2 ) * ⁡ [ K - k ] ] ⁢ ⁢ W = [ α 1 , 1 0 0 0 0 α 1 , 2 0 0 00 α 2 , 1 0 0 0 0 α 2 , 2 ] ⁢ = Δ ⁢ [ w ( 1 , 1 ) w ( 1 , 2 ) w ( 2 , 1 ) ⁢  w ( 2 , 2 ) ] ( 4 )

where α_(i,j) are constants and

_(w) ^((i,j))εC^(2×2). This method is beneficial because using thisprecoder, it is possible to keep other aspects of the DIDO precoder thesame as before, since the effects of I/Q gain and phase imbalance arecompletely cancelled at the transmitter.

It is also possible to design DIDO precoders that pre-cancel inter-userinterference, without pre-cancelling ICI due to IQ imbalance. With thisapproach, the receiver (instead of the transmitter) compensates for theIQ imbalance by employing one of the receive filters described below.Then, the pre-coding design criterion in (4) can be modified as w ⁢ = Δ ⁢[ H e ( 1 ) ⁡ [ k ] H c ( 1 ) ⁡ [ k ] H c ( 1 ) * ⁡ [ K - k ] H e ( 1 ) * ⁡[ K - k ] H e ( 2 ) ⁡ [ k ] H c ( 2 ) ⁡ [ k ] H c ( 2 ) * ⁡ [ K - k ] H e (2 ) * ⁡ [ K - k ] ] ⁢ ⁢ W = [ α 1 , 1 α 1 , 2 0 0 α 2 , 1 α 2 , 2 0 0 0 0 α3 , 3 α 3 , 4 0 0 α 4 , 3 α 4 , 4 ] ⁢ = Δ ⁢ [ w ( 1 , 1 ) w ( 1 , 2 ) w (2 , 1 ) w ( 2 , 2 ) ] ( 5 ) x _ 1 ⁡ [ k ] = [   ⁢ w ( 1 , 1 ) ⁢   ⁢ w ( 1 ,2 ) ] ⁡ [ s _ 1 ⁡ [ k ] s _ 2 ⁡ [ k ] ] ⁢ ⁢ and ( 6 ) x _ 2 ⁡ [ k ] = [   ⁢ w (2 , 1 ) ⁢   ⁢ w ( 2 , 2 ) ] ⁡ [ s _ 1 ⁡ [ k ] s _ 2 ⁡ [ k ] ] ( 7 )

where s _(m)[k]=[ s _(m)[k], s*_(m)[K−k]]^(T) for the m-th transmitsymbol and x _(m)[k]=[ x _(m)[k], x*_(m)[K−k]]^(T) is the receive symbolvector for user m.

At the receive side, to estimate the transmit symbol vector s _(m)[k],user m employs ZF filter and the estimated symbol vector is given by s ^m ( ZF ) ⁡ [ k ] = [ ( w ( m , m ) ⁢ † ⁢ w ( m , m ) ) - 1 ⁢ w ( m , m ) ⁢ †] ⁢ x _ m ⁡ [ k ] ( 8 )

While the ZF filter is the easiest to understand, the receiver may applyany number of other filters known to those skilled in the art. Onepopular choice is the MMSE filter where s ^ m ( MMSE ) ⁡ [ k ] = ( w ( m, m ) ⁢ † + p - 1 ⁢ I ) - 1 ⁢ w ( m , m ) ⁢ w ( m , m ) ⁢ † ⁢ x _ m ⁡ [ k ] ( 9)and ρ is the signal-to-noise ratio. Alternatively, the receiver mayperform a maximum likelihood symbol detection (or sphere decoder oriterative variation). For example, the first user might use the MLreceiver and solve the following optimization s ^ m ( ML ) ⁡ [ k ] = arg ⁢  ⁢ min s 1 , s 2 ∈ S ⁢  y _ 1 ⁡ [ k ] - [ w ( 1 , 1 ) ⁢ w ( 1 , 2 ) ] ⁡ [ s1 ⁡ [ k ] s 2 ⁡ [ k ] ]  ( 10 )

where S is the set of all possible vectors s and depends on theconstellation size. The ML receiver gives better performance at theexpense of requiring more complexity at the receiver. A similar set ofequations applies for the second user.

Note that

_(w) ^((1,2)) and

_(w) ^((2,1)) in (6) and (7) are assumed to have zero entries. Thisassumption holds only if the transmit precoder is able to cancelcompletely the inter-user interference as for the criterion in (4).Similarly

_(w) ^((1,1)) and

_(w) ^((2,2)) are diagonal matrices only if the transmit precoder isable to cancel completely the inter-carrier interference (i.e., from themirror tones).

FIG. 13 illustrates one embodiment of a framework for DIDO-OFDM systemswith I/Q compensation including IQ-DIDO precoder 1302 within a BaseStation (BS), a transmission channel 1304, channel estimation logic 1306within a user device, and a ZF, MMSE or ML receiver 1308. The channelestimation logic 1306 estimates the channels

_(e) ^((m)) and

_(c) ^((m)) via training symbols and feedbacks these estimates to theprecoder 1302 within the AP. The BS computes the DIDO precoder weights(matrix W) to pre-cancel the interference due to I/Q gain and phaseimbalance as well as inter-user interference and transmits the data tothe users through the wireless channel 1304. User device m employs theZF, MMSE or ML receiver 1308, by exploiting the channel estimatesprovided by the unit 1304, to cancel residual interference anddemodulates the data.

The following three embodiments may be employed to implement this I/Qcompensation algorithm:

Method 1—TX compensation: In this embodiment, the transmitter calculatesthe pre-coding matrix according to the criterion in (4). At thereceiver, the user devices employ a “simplified” ZF receiver, where

_(w) ^((1,1)) and

_(w) ^((2,2)) are assumed to be diagonal matrices. Hence, equation (8)simplifies as $\begin{matrix}{{{\hat{s}}_{m}\lbrack k\rbrack} = {\begin{bmatrix}{1/\alpha_{m,1}} & 0 \\0 & {1/\alpha_{m,2}}\end{bmatrix}{{{\overset{\_}{x}}_{m}\lbrack k\rbrack}.}}} & (10)\end{matrix}$

Method 2—RX compensation: In this embodiment, the transmitter calculatesthe pre-coding matrix based on the conventional BD method described inR. Chen, R. W. Heath, and J. G. Andrews, “Transmit selection diversityfor unitary precoded multiuser spatial multiplexing systems with linearreceivers,” accepted to IEEE Trans. on Signal Processing, 2005, withoutcanceling inter-carrier and inter-user interference as for the criterionin (4). With this method, the pre-coding matrix in (2) and (3)simplifies as $\begin{matrix}{W = {\begin{bmatrix}{w_{1,1}\lbrack k\rbrack} & 0 & {w_{1,2}\lbrack k\rbrack} & 0 \\0 & {w_{1,1}^{*}\left\lbrack {K - k} \right\rbrack} & 0 & {w_{1,2}^{*}\left\lbrack {K - k} \right\rbrack} \\{w_{2,1}\lbrack k\rbrack} & 0 & {w_{2,2}\lbrack k\rbrack} & 0 \\0 & {w_{2,1}^{*}\left\lbrack {K - k} \right\rbrack} & 0 & {w_{2,2}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}.}} & (12)\end{matrix}$

At the receiver, the user devices employ a ZF filter as in (8). Notethat this method does not pre-cancel the interference at the transmitteras in the method 1 above. Hence, it cancels the inter-carrierinterference at the receiver, but it is not able to cancel theinter-user interference. Moreover, in method 2 the users only need tofeedback the vectors

_(e) ^((m)) for the transmitter to compute the DIDO precoder, as opposedto method 1 that requires feedback of both

_(e) ^((m)) and

_(c) ^((m)). Therefore, method 2 is particularly suitable for DIDOsystems with low rate feedback channels. On the other hand, method 2requires slightly higher computational complexity at the user device tocompute the ZF receiver in (8) rather than (11).

Method 3—TX-RX compensation: In one embodiment, the two methodsdescribed above are combined. The transmitter calculates the pre-codingmatrix as in (4) and the receivers estimate the transmit symbolsaccording to (8).

I/Q imbalance, whether phase imbalance, gain imbalance, or delayimbalance, creates a deleterious degradation in signal quality inwireless communication systems. For this reason, circuit hardware in thepast was designed to have very low imbalance. As described above,however, it is possible to correct this problem using digital signalprocessing in the form of transmit pre-coding and/or a special receiver.One embodiment of the invention comprises a system with several newfunctional units, each of which is important for the implementation ofI/Q correction in an OFDM communication system or a DIDO-OFDMcommunication system.

One embodiment of the invention uses pre-coding based on channel stateinformation to cancel inter-carrier interference (ICI) from mirror tones(due to I/Q mismatch) in an OFDM system. As illustrated in FIG. 11, aDIDO transmitter according to this embodiment includes a user selectorunit 1102, a plurality of coding modulation units 1104, a correspondingplurality of mapping units 1106, a DIDO IQ-aware precoding unit 1108, aplurality of RF transmitter units 1114, a user feedback unit 1112 and aDIDO configurator unit 1110.

The user selector unit 1102 selects data associated with a plurality ofusers U₁-U_(M), based on the feedback information obtained by thefeedback unit 1112, and provides this information each of the pluralityof coding modulation units 1104. Each coding modulation unit 1104encodes and modulates the information bits of each user and send them tothe mapping unit 1106. The mapping unit 1106 maps the input bits tocomplex symbols and sends the results to the DIDO IQ-aware precedingunit 1108. The DIDO IQ-aware precoding unit 1108 exploits the channelstate information obtained by the feedback unit 1112 from the users tocompute the DIDO IQ-aware preceding weights and precoding the inputsymbols obtained from the mapping units 1106. Each of the precoded datastreams is sent by the DIDO IQ-aware preceding unit 1108 to the OFDMunit 1115 that computes the IFFT and adds the cyclic prefix. Thisinformation is sent to the D/A unit 1116 that operates the digital toanalog conversion and send it to the RF unit 1114. The RF unit 1114upconverts the baseband signal to intermediate/radio frequency and sendit to the transmit antenna.

The precoder operates on the regular and mirror tones together for thepurpose of compensating for I/Q imbalance. Any number of precoder designcriteria may be used including ZF, MMSE, or weighted MMSE design. In apreferred embodiment, the precoder completely removes the ICI due to I/Qmismatch thus resulting in the receiver not having to perform anyadditional compensation.

In one embodiment, the precoder uses a block diagonalization criterionto completely cancel inter-user interference while not completelycanceling the I/Q effects for each user, requiring additional receiverprocessing. In another embodiment, the precoder uses a zero-forcingcriterion to completely cancel both inter-user interference and ICI dueto I/Q imbalance. This embodiment can use a conventional DIDO-OFDMprocessor at the receiver.

One embodiment of the invention uses pre-coding based on channel stateinformation to cancel inter-carrier interference (ICI) from mirror tones(due to I/Q mismatch) in a DIDO-OFDM system and each user employs anIQ-aware DIDO receiver. As illustrated in FIG. 12, in one embodiment ofthe invention, a system including the receiver 1202 includes a pluralityof RF units 1208, a corresponding plurality of A/D units 1210, anIQ-aware channel estimator unit 1204 and a DIDO feedback generator unit1206.

The RF units 1208 receive signals transmitted from the DIDO transmitterunits 1114, downconverts the signals to baseband and provide thedownconverted signals to the A/D units 1210. The A/D units 1210 thenconvert the signal from analog to digital and send it to the OFDM units1213. The OFDM units 1213 remove the cyclic prefix and operates the FFTto report the signal to the frequency domain. During the training periodthe OFDM units 1213 send the output to the IQ-aware channel estimateunit 1204 that computes the channel estimates in the frequency domain.Alternatively, the channel estimates can be computed in the time domain.During the data period the OFDM units 1213 send the output to theIQ-aware receiver unit 1202. The IQ-aware receiver unit 1202 computesthe IQ receiver and demodulates/decodes the signal to obtain the data1214. The IQ-aware channel estimate unit 1204 sends the channelestimates to the DIDO feedback generator unit 1206 that may quantize thechannel estimates and send it back to the transmitter via the feedbackcontrol channel 1112.

The receiver 1202 illustrated in FIG. 12 may operate under any number ofcriteria known to those skilled in the art including ZF, MMSE, maximumlikelihood, or MAP receiver. In one preferred embodiment, the receiveruses an MMSE filter to cancel the ICI caused by IQ imbalance on themirror tones. In another preferred embodiment, the receiver uses anonlinear detector like a maximum likelihood search to jointly detectthe symbols on the mirror tones. This method has improved performance atthe expense of higher complexity.

In one embodiment, an IQ-aware channel estimator 1204 is used todetermine the receiver coefficients to remove ICI. Consequently we claima DIDO-OFDM system that uses pre-coding based on channel stateinformation to cancel inter-carrier interference (ICI) from mirror tones(due to I/Q mismatch), an IQ-aware DIDO receiver, and an IQ-awarechannel estimator. The channel estimator may use a conventional trainingsignal or may use specially constructed training signals sent on theinphase and quadrature signals. Any number of estimation algorithms maybe implemented including least squares, MMSE, or maximum likelihood. TheIQ-aware channel estimator provides an input for the IQ-aware receiver.

Channel state information can be provided to the station through channelreciprocity or through a feedback channel. One embodiment of theinvention comprises a DIDO-OFDM system, with I/Q-aware precoder, with anI/Q-aware feedback channel for conveying channel state information fromthe user terminals to the station. The feedback channel may be aphysical or logical control channel. It may be dedicated or shared, asin a random access channel. The feedback information may be generatedusing a DIDO feedback generator at the user terminal, which we alsoclaim. The DIDO feedback generator takes as an input the output of theI/Q aware channel estimator. It may quantize the channel coefficients ormay use any number of limited feedback algorithms known in the art.

The allocation of users, modulation and coding rate, mapping tospace-time-frequency code slots may change depending on the results ofthe DIDO feedback generator. Thus, one embodiment comprises an IQ-awareDIDO configurator that uses an IQ-aware channel estimate from one ormore users to configure the DIDO IQ-aware precoder, choose themodulation rate, coding rate, subset of users allowed to transmit, andtheir mappings to space-time-frequency code slots.

To evaluate the performance of the proposed compensation methods, threeDIDO 2×2 systems will be compared:

1. With I/Q mismatch: transmit over all the tones (except DC and edgetones), without compensation for I/Q mismatch;

2. With I/Q compensation: transmit over all the tones and compensate forI/Q mismatch by using the “method 1” described above;

3. Ideal: transmit data only over the odd tones to avoid inter-user andinter-carrier (i.e., from the mirror tones) interference caused to I/Qmismatch.

Hereafter, results obtained from measurements with the DIDO-OFDMprototype in real propagation scenarios are presented. FIG. 14 depictsthe 64-QAM constellations obtained from the three systems describedabove. These constellations are obtained with the same users' locationsand fixed average signal-to-noise ratio (˜45 dB). The firstconstellation 1401 is very noisy due to interference from the mirrortones caused by I/Q imbalance. The second constellation 1402 shows someimprovements due to I/Q compensations. Note that the secondconstellation 1402 is not as clean as the ideal case shown asconstellation 1403 due to possible phase noise that yields inter-carrierinterference (ICI).

FIG. 15 shows the average SER (Symbol Error Rate) 1501 and per-usergoodput 1502 performance of DIDO 2×2 systems with 64-QAM and coding rate¾, with and without I/Q mismatch. The OFDM bandwidth is 250 KHz, with 64tones and cyclic prefix length L_(cp)=4. Since in the ideal case wetransmit data only over a subset of tones, SER and goodput performanceis evaluated as a function of the average per-tone transmit power(rather than total transmit power) to guarantee a fair comparison acrossdifferent cases. Moreover, in the following results, we use normalizedvalues of transmit power (expressed in decibel), since our goal here isto compare the relative (rather than absolute) performance of differentschemes. FIG. 15 shows that in presence of I/Q imbalance the SERsaturates, without reaching the target SER (˜10⁻²), consistently to theresults reported in A. Tarighat and A. H. Sayed, “MIMO OFDM receiversfor systems with IQ imbalances,” IEEE Trans. Sig. Proc., vol. 53, pp.3583-3596, September 2005. This saturation effect is due to the factthat both signal and interference (from the mirror tones) power increaseas the TX power increases. Through the proposed I/Q compensation method,however, it is possible to cancel the interference and obtain better SERperformance. Note that the slight increase in SER at high SNR is due toamplitude saturation effects in the DAC, due to the larger transmitpower required for 64-QAM modulations.

Moreover, observe that the SER performance with I/Q compensation is veryclose to the ideal case. The 2 dB gap in TX power between these twocases is due to possible phase noise that yields additional interferencebetween adjacent OFDM tones. Finally, the goodput curves 1502 show thatit is possible to transmit twice as much data when the I/Q method isapplied compared to the ideal case, since we use all the data tonesrather than only the odd tones (as for the ideal case).

FIG. 16 graphs the SER performance of different QAM constellations withand without I/Q compensation. We observe that, in this embodiment, theproposed method is particularly beneficial for 64-QAM constellations.For 4-QAM and 16-QAM the method for I/Q compensation yields worseperformance than the case with I/Q mismatch, possibly because theproposed method requires larger power to enable both data transmissionand interference cancellation from the mirror tones. Moreover, 4-QAM and16-QAM are not as affected by I/Q mismatch as 64-QAM due to the largerminimum distance between constellation points. See A. Tarighat, R.Bagheri, and A. H. Sayed, “Compensation schemes and performance analysisof IQ imbalances in OFDM receivers,” Signal Processing, IEEETransactions on [see also Acoustics, Speech, and Signal Processing, IEEETransactions on], vol. 53, pp. 3257-3268, August 2005. This can be alsoobserved in FIG. 16 by comparing the I/Q mismatch against the ideal casefor 4-QAM and 16-QAM. Hence, the additional power required by the DIDOprecoder with interference cancellation (from the mirror tones) does notjustify the small benefit of the I/Q compensation for the cases of 4-QAMand 16-QAM. Note that this issue may be fixed by employing the methods 2and 3 for I/Q compensation described above.

Finally, the relative SER performance of the three methods describedabove is measured in different propagation conditions. For reference,also described is the SER performance in presence of I/Q mismatch. FIG.17 depicts the SER measured for a DIDO 2×2 system with 64-QAM at carrierfrequency of 450.5 MHz and bandwidth of 250 KHz, at two different users'locations. In Location 1 the users are ˜6λ from the BS in differentrooms and NLOS (Non-Line of Sight)) conditions. In Location 2 the usersare ˜λ from the BS in LOS (Line of Sight).

FIG. 17 shows that all three compensation methods always outperform thecase of no compensation. Moreover, it should be noted that method 3outperforms the other two compensation methods in any channel scenario.The relative performance of method 1 and 2 depends on the propagationconditions. It is observed through practical measurement campaigns thatmethod 1 generally outperforms method 2, since it pre-cancels (at thetransmitter) the inter-user interference caused by I/Q imbalance. Whenthis inter-user interference is minimal, method 2 may outperform method1 as illustrated in graph 1702 of FIG. 17, since it does not suffer frompower loss due to the I/Q compensation precoder.

So far, different methods have been compared by considering only alimited set of propagation scenarios as in FIG. 17. Hereafter, therelative performance of these methods in ideal i.i.d. (independent andidentically-distributed) channels is measured. DIDO-OFDM systems aresimulated with I/Q phase and gain imbalance at the transmit and receivesides. FIG. 18 shows the performance of the proposed methods with onlygain imbalance at the transmit side (i.e., with 0.8 gain on the I railof the first transmit chain and gain 1 on the other rails). It isobserved that method 3 outperforms all the other methods. Also, method 1performs better than method 2 in i.i.d. channels, as opposed to theresults obtained in Location 2 in graph 1702 of FIG. 17.

Thus, given the three novel methods to compensate for I/Q imbalance inDIDO-OFDM systems described above, Method 3 outperforms the otherproposed compensation methods. In systems with low rate feedbackchannels, method 2 can be used to reduce the amount of feedback requiredfor the DIDO precoder, at the expense of worse SER performance.

II. Adaptive DIDO Transmission Scheme

Another embodiment of a system and method to enhance the performance ofdistributed-input distributed-output (DIDO) systems will now bedescribed. This method dynamically allocates the wireless resources todifferent user devices, by tracking the changing channel conditions, toincrease throughput while satisfying certain target error rate. The userdevices estimate the channel quality and feedback it to the Base Station(BS); the Base Station processes the channel quality obtained from theuser devices to select the best set of user devices, DIDO scheme,modulation/coding scheme (MCS) and array configuration for the nexttransmission; the Base Station transmits parallel data to multiple userdevices via pre-coding and the signals are demodulated at the receiver.

A system that efficiently allocates resources for a DIDO wireless linkis also described. The system includes a DIDO Base Station with a DIDOconfigurator, which processes feedback received from the users to selectthe best set of users, DIDO scheme, modulation/coding scheme (MCS) andarray configuration for the next transmission; a receiver in a DIDOsystem that measures the channel and other relevant parameters togenerate a DIDO feedback signal; and a DIDO feedback control channel forconveying feedback information from users to the Base Station.

As described in detail below, some of the significant features of thisembodiment of the invention include, but are not limited to:

Techniques to adaptively select number of users, DIDO transmissionschemes (i.e., antenna selection or multiplexing), modulation/codingscheme (MCS) and array configurations based on the channel qualityinformation, to minimize SER or maximize per-user or downlink spectralefficiency;

Techniques to define sets of DIDO transmission modes as combinations ofDIDO schemes and MCSs;

Techniques to assign different DIDO modes to different time slots, OFDMtones and DIDO substreams, depending on the channel conditions;

Techniques to dynamically assign different DIDO modes to different usersbased on their channel quality;

Criterion to enable adaptive DIDO switching based on link qualitymetrics computed in the time, frequency and space domains;

Criterion to enable adaptive DIDO switching based on lookup tables.

A DIDO system with a DIDO configurator at the Base Station as in FIG. 19to adaptively select the number of users, DIDO transmission schemes(i.e., antenna selection or multiplexing), modulation/coding scheme(MCS) and array configurations based on the channel quality information,to minimize SER or maximize per user or downlink spectral efficiency;

A DIDO system with a DIDO configurator at the Base Station and a DIDOfeedback generator at each user device as in FIG. 20, which uses theestimated channel state and/or other parameters like the estimated SNRat the receiver to generate a feedback message to be input into the DIDOconfigurator.

A DIDO system with a DIDO configurator at the Base Station, DIDOfeedback generator, and a DIDO feedback control channel for conveyingDIDO-specific configuration information from the users to the BaseStation.

a. Background

In multiple-input multiple-output (MIMO) systems, diversity schemes suchas orthogonal space-time block codes (OSTBC) (See V. Tarokh, H.Jafarkhani, and A. R. Calderbank, “Spacetime block codes from orthogonaldesigns,” IEEE Trans. Info. Th., vol. 45, pp. 1456-467, Jul. 1999) orantenna selection (See R. W. Heath Jr., S. Sandhu, and A. J. Pauiraj,“Antenna selection for spatial multiplexing systems with linearreceivers,” IEEE Trans. Comm., vol. 5, pp. 142-144, April 2001) areconceived to combat channel fading, providing increased link robustnessthat translates in better coverage. On the other hand, spatialmultiplexing (SM) enables transmission of multiple parallel data streamsas a means to enhance systems throughput. See G. J. Foschini, G. D.Golden, R. A. Valenzuela, and P. W. Wolniansky, “Simplified processingfor high spectral e□ciency wireless communication employing multielementarrays,” IEEE Jour. Select. Areas in Comm., vol. 17, no. 11, pp.1841-1852, Nov. 1999. These benefits can be simultaneously achieved inMIMO systems, according to the theoretical diversity/multiplexingtradeoffs derived in L. Zheng and D. N. C. Tse, “Diversity andmultiplexing: a fundamental tradeoff in multiple antenna channels,” IEEETrans. Info. Th., vol. 49, no. 5, pp. 1073-1096, May 2003. One practicalimplementation is to adaptively switch between diversity andmultiplexing transmission schemes, by tracking the changing channelconditions.

A number of adaptive MIMO transmission techniques have been proposedthus far. The diversity/multiplexing switching method in R. W. Heath andA. J. Paulraj, “Switching between diversity and multiplexing in MIMOsystems,” IEEE Trans. Comm., vol. 53, no. 6, pp. 962-968, June 2005, wasdesigned to improve BER (Bit Error Rate) for fixed rate transmission,based on instantaneous channel quality information. Alternatively,statistical channel information can be employed to enable adaptation asin S. Catreux, V. Erceg, D. Gesbert, and R. W. Heath. Jr., “Adaptivemodulation and MIMO coding for broadband wireless data networks,” IEEEComm. Mag., vol. 2, pp. 108-115, June 2002 (“Catreux”), resulting inreduced feedback overhead and number of control messages. The adaptivetransmission algorithm in Catreux was designed to enhance spectralefficiency for predefined target error rate in orthogonal frequencydivision multiplexing (OFDM) systems, based on channel time/frequencyselectivity indicators. Similar low feedback adaptive approaches havebeen proposed for narrowband systems, exploiting the channel spatialselectivity to switch between diversity schemes and spatialmultiplexing. See, e.g., A. Forenza, M. R. McKay, A. Pandharipande, R.W. Heath. Jr., and I. B. Collings, “Adaptive MIMO transmission forexploiting the capacity of spatially correlated channels,” accepted tothe IEEE Trans. on Veh. Tech., March 2007; M. R. McKay, I. B. Collings,A. Forenza, and R. W. Heath. Jr., “Multiplexing/beamforming switchingfor coded MIMO in spatially correlated Rayleigh channels,” accepted tothe IEEE Trans. on Veh. Tech., December 2007; A. Forenza, M. R. McKay,R. W. Heath. Jr., and I. B. Collings, “Switching between OSTBC andspatial multiplexing with linear receivers in spatially correlated MIMOchannels,” Proc. IEEE Veh. Technol. Conf., vol. 3, pp. 1387-1391, May2006; M. R. McKay, I. B. Collings, A. Forenza, and R. W. Heath Jr., “Athroughput-based adaptive MIMO BICM approach for spatially correlatedchannels,” to appear in Proc. IEEE ICC, June 2006

In this document, we extend the scope of the work presented in variousprior publications to DIDO-OFDM systems. See, e.g., R. W. Heath and A.J. Pauiraj, “Switching between diversity and multiplexing in MIMOsystems,” IEEE Trans. Comm., vol. 53, no. 6, pp. 962-968, June 2005. S.Catreux, V. Erceg, D. Gesbert, and R. W. Heath Jr., “Adaptive modulationand MIMO coding for broadband wireless data networks,” IEEE Comm. Mag.,vol. 2, pp. 108-115, June 2002; A. Forenza, M. R. McKay, A.Pandharipande, R. W. Heath Jr., and I. B. Collings, “Adaptive MIMOtransmission for exploiting the capacity of spatially correlatedchannels,” IEEE Trans. on Veh. Tech., vol. 56, n. 2, pp. 619-630, March2007. M. R. McKay, I. B. Collings, A. Forenza, and R. W. Heath Jr.,“Multiplexing/beamforming switching for coded MIMO in spatiallycorrelated Rayleigh channels,” accepted to the IEEE Trans. on Veh.Tech., December 2007; A. Forenza, M. R. McKay, R. W. Heath Jr., and I.B. Collings, “Switching between OSTBC and spatial multiplexing withlinear receivers in spatially correlated MIMO channels,” Proc. IEEE Veh.Technol. Conf., vol. 3, pp. 1387-1391, May 2006. M. R. McKay, I. B.Collings, A. Forenza, and R. W. Heath Jr., “A throughput-based adaptiveMIMO BICM approach for spatially correlated channels,” to appear inProc. IEEE ICC, June 2006.

A novel adaptive DIDO transmission strategy is described herein thatswitches between different numbers of users, numbers of transmitantennas and transmission schemes based on channel quality informationas a means to improve the system performance. Note that schemes thatadaptively select the users in multiuser MIMO systems were alreadyproposed in M. Sharif and B. Hassibi, “On the capacity of MIMO broadcastchannel with partial side information,” IEEE Trans. Info. Th., vol. 51,p. 506522, February 2005; and W. Choi, A. Forenza, J. G. Andrews, and R.W. Heath Jr., “Opportunistic space division multiple access with beamselection,” to appear in IEEE Trans. on Communications. Theopportunistic space division multiple access (OSDMA) schemes in thesepublications, however, are designed to maximize the sum capacity byexploiting multi-user diversity and they achieve only a fraction of thetheoretical capacity of dirty paper codes, since the interference is notcompletely pre-canceled at the transmitter. In the DIDO transmissionalgorithm described herein block diagonalization is employed topre-cancel inter-user interference. The proposed adaptive transmissionstrategy, however, can be applied to any DIDO system, independently onthe type of pre-coding technique.

The present patent application describes an extension of the embodimentsof the invention described above and in the Prior Application,including, but not limited to the following additional features:

1. The training symbols of the Prior Application for channel estimationcan be employed by the wireless client devices to evaluate thelink-quality metrics in the adaptive DIDO scheme;

2. The base station receives signal characterization data from theclient devices as described in the Prior Application. In the currentembodiment, the signal characterization data is defined as link-qualitymetric used to enable adaptation;

3. The Prior Application describes a mechanism to select the number oftransmit antennas and users as well as defines throughput allocation.Moreover, different levels of throughput can be dynamically assigned todifferent clients as in the Prior Application. The current embodiment ofthe invention defines novel criteria related to this selection andthroughput allocation.

b. Embodiments of the Invention

The goal of the proposed adaptive DIDO technique is to enhance per-useror downlink spectral efficiency by dynamically allocating the wirelessresource in time, frequency and space to different users in the system.The general adaptation criterion is to increase throughput whilesatisfying the target error rate. Depending on the propagationconditions, this adaptive algorithm can also be used to improve the linkquality of the users (or coverage) via diversity schemes. The flowchartillustrated in FIG. 21 describes steps of the adaptive DIDO scheme.

The Base Station (BS) collects the channel state information (CSI) fromall the users in 2102. From the received CSI, the BS computes the linkquality metrics in time/frequency/space domains in 2104. These linkquality metrics are used to select the users to be served in the nexttransmission as well as the transmission mode for each of the users in2106. Note that the transmission modes consist of different combinationsof modulation/coding and DIDO schemes. Finally, the BS transmits data tothe users via DIDO preceding as in 2108.

At 2102, the Base Station collects the channel state information (CSI)from all the user devices. The CSI is used by the Base Station todetermine the instantaneous or statistical channel quality for all theuser devices at 2104. In DIDO-OFDM systems the channel quality (or linkquality metric) can be estimated in the time, frequency and spacedomains. Then, at 2106, the Base Station uses the link quality metric todetermine the best subset of users and transmission mode for the currentpropagation conditions. A set of DIDO transmission modes is defined ascombinations of DIDO schemes (i.e., antenna selection or multiplexing),modulation/coding schemes (MCSs) and array configuration. At 2108, datais transmitted to user devices using the selected number of users andtransmission modes.

The mode selection is enabled by lookup tables (LUTs) pre-computed basedon error rate performance of DIDO systems in different propagationenvironments. These LUTs map channel quality information into error rateperformance. To construct the LUTs, the error rate performance of DIDOsystems is evaluated in different propagation scenarios as a function ofthe SNR. From the error rate curves, it is possible to compute theminimum SNR required to achieve certain pre-defined target error rate.We define this SNR requirement as SNR threshold. Then, the SNRthresholds are evaluated in different propagation scenarios and fordifferent DIDO transmission modes and stored in the LUTs. For example,the SER results in FIGS. 24 and 26 can be used to construct the LUTs.Then, from the LUTs, the Base Station selects the transmission modes forthe active users that increase throughput while satisfying predefinedtarget error rate. Finally, the Base Station transmits data to theselected users via DIDO pre-coding. Note that different DIDO modes canbe assigned to different time slots, OFDM tones and DIDO substreams suchthat the adaptation may occur in time, frequency and space domains.

One embodiment of a system employing DIDO adaptation is illustrated inFIGS. 19-20. Several new functional units are introduced to enableimplementation of the proposed DIDO adaptation algorithms. Specifically,in one embodiment, a DIDO configurator 1910 performs a plurality offunctions including selecting the number of users, DIDO transmissionschemes (i.e., antenna selection or multiplexing), modulation/codingscheme (MCS), and array configurations based on the channel qualityinformation 1912 provided by user devices.

The user selector unit 1902 selects data associated with a plurality ofusers U₁-U_(M), based on the feedback information obtained by the DIDOconfigurator 1910, and provides this information each of the pluralityof coding modulation units 1904. Each coding modulation unit 1904encodes and modulates the information bits of each user and sends themto the mapping unit 1906. The mapping unit 1906 maps the input bits tocomplex symbols and sends it to the precoding unit 1908. Both the codingmodulation units 1904 and the mapping unit 1906 exploit the informationobtained from the DIDO configurator unit 1910 to choose the type ofmodulation/coding scheme to employ for each user. This information iscomputed by the DIDO configurator unit 1910 by exploiting the channelquality information of each of the users as provided by the feedbackunit 1912. The DIDO preceding unit 1908 exploits the informationobtained by the DIDO configurator unit 1910 to compute the DIDOpreceding weights and precoding the input symbols obtained from themapping units 1906. Each of the precoded data streams are sent by theDIDO precoding unit 1908 to the OFDM unit 1915 that computes the IFFTand adds the cyclic prefix. This information is sent to the D/A unit1916 that operates the digital to analog conversion and sends theresulting analog signal to the RF unit 1914. The RF unit 1914 upconvertsthe baseband signal to intermediate/radio frequency and send it to thetransmit antenna.

The RF units 2008 of each client device receive signals transmitted fromthe DIDO transmitter units 1914, downconverts the signals to basebandand provide the downconverted signals to the A/D units 2010. The A/Dunits 2010 then convert the signal from analog to digital and send it tothe OFDM units 2013. The OFDM units 2013 remove the cyclic prefix andcarries out the FFT to report the signal to the frequency domain. Duringthe training period the OFDM units 2013 send the output to the channelestimate unit 2004 that computes the channel estimates in the frequencydomain. Alternatively, the channel estimates can be computed in the timedomain. During the data period the OFDM units 2013 send the output tothe receiver unit 2002 which demodulates/decodes the signal to obtainthe data 2014. The channel estimate unit 2004 sends the channelestimates to the DIDO feedback generator unit 2006 that may quantize thechannel estimates and send it back to the transmitter via the feedbackcontrol channel 1912.

The DIDO configurator 1910 may use information derived at the BaseStation or, in a preferred embodiment, uses additionally the output of aDIDO Feedback Generator 2006 (see FIG. 20), operating at each userdevice. The DIDO Feedback Generator 2006 uses the estimated channelstate 2004 and/or other parameters like the estimated SNR at thereceiver to generate a feedback message to be input into the DIDOConfigurator 1910. The DIDO Feedback Generator 2006 may compressinformation at the receiver, may quantize information, and/or use somelimited feedback strategies known in the art.

The DIDO Configurator 1910 may use information recovered from a DIDOFeedback Control Channel 1912. The DIDO Feedback Control Channel 1912 isa logical or physical control channel that is used to send the output ofthe DIDO Feedback Generator 2006 from the user to the Base Station. Thecontrol channel 1912 may be implemented in any number of ways known inthe art and may be a logical or a physical control channel. As aphysical channel it may comprise a dedicated time/frequency slotassigned to a user. It may also be a random access channel shared by allusers. The control channel may be pre-assigned or it may be created bystealing bits in a predefined way from an existing control channel.

In the following discussion, results obtained through measurements withthe DIDO-OFDM prototype are described in real propagation environments.These results demonstrate the potential gains achievable in adaptiveDIDO systems. The performance of different order DIDO systems ispresented initially, demonstrating that it is possible to increase thenumber of antennas/user to achieve larger downlink throughput. The DIDOperformance as a function of user device's location is then described,demonstrating the need for tracking the changing channel conditions.Finally, the performance of DIDO systems employing diversity techniquesis described.

i. Performance of Different Order DIDO Systems

The performance of different DIDO systems is evaluated with increasingnumber of transmit antennas N=M, where M is the number of users. Theperformance of the following systems is compared: SISO, DIDO 2×2, DIDO4×4, DIDO 6×6 and DIDO 8×8. DIDO N×M refers to DIDO with N transmitantennas at the BS and M users.

FIG. 22 illustrates the transmit/receive antenna layout. The transmitantennas 2201 are placed in squared array configuration and the usersare located around the transmit array. In FIG. 22, T indicates the“transmit” antennas and U refers to the “user devices” 2202.

Different antenna subsets are active in the 8-element transmit array,depending on the value of N chosen for different measurements. For eachDIDO order (N) the subset of antennas that covers the largest realestate for fixed size constraint of the 8-element array was chosen. Thiscriterion is expected to enhance the spatial diversity for any givenvalue of N.

FIG. 23 shows the array configurations for different DIDO orders thatfit the available real estate (i.e., dashed line). The squared dashedbox has dimensions of 24″×24″, corresponding to ˜λ×λ at the carrierfrequency of 450 MHz.

Based on the comments related to FIG. 23 and with reference to FIG. 22,the performance of each of the following systems will now be defined andcompared:

SISO with T1 and U1 (2301)

DIDO 2×2 with T1,2 and U1,2 (2302)

DIDO 4×4 with T1,2,3,4 and U1,2,3,4 (2303)

DIDO 6×6 with T1,2,3,4,5,6 and U1,2,3,4,5,6 (2304)

DIDO 8×8 with T1,2,3,4,5,6,7,8 and U1,2,3,4,5,6,7,8 (2305)

FIG. 24 shows the SER, BER, SE (Spectral Efficiency) and goodputperformance as a function of the transmit (TX) power for the DIDOsystems described above, with 4-QAM and FEC (Forward Error Correction)rate of ½. Observe that the SER and BER performance degrades forincreasing values of N. This effect is due to two phenomena: for fixedTX power, the input power to the DIDO array is split between increasingnumber of users (or data streams); the spatial diversity decreases withincreasing number of users in realistic (spatially correlated) DIDOchannels.

To compare the relative performance of different order DIDO systems thetarget BER is fixed to 10⁻⁴ (this value may vary depending on thesystem) that corresponds approximately to SER=10⁻² as shown in FIG. 24.We refer to the TX power values corresponding to this target as TX powerthresholds (TPT). For any N, if the TX power is below the TPT, we assumeit is not possible to transmit with DIDO order N and we need to switchto lower order DIDO. Also, in FIG. 24, observe that the SE and goodputperformance saturate when the TX power exceeds the TPTs for any value ofN. From these results, an adaptive transmission strategy may be designedthat switches between different order DIDO to enhance SE or goodput forfixed predefined target error rate.

ii. Performance with Variable User Location

The goal of this experiment is to evaluate the DIDO performance fordifferent users' location, via simulations in spatially correlatedchannels. DIDO 2×2 systems are considered with 4QAM and an FEC rate of½. User 1 is at a broadside direction from the transmit array, whereasuser 2 changes locations from broadside to endfire directions asillustrated in FIG. 25. The transmit antennas are spaced ˜λ/2 andseparated ˜2.5λ from the users.

FIG. 26 shows the SER and per-user SE results for different locations ofuser device 2. The user device's angles of arrival (AOAs) range between0° and 90°, measured from the broadside direction of the transmit array.Observe that, as the user device's angular separation increases, theDIDO performance improves, due to larger diversity available in the DIDOchannel. Also, at target SER=10⁻² there is a 10 dB gap between the casesAOA2=0° and AOA2=90°. This result is consistent to the simulationresults obtained in FIG. 35 for an angle spread of 10°. Also, note thatfor the case of AOA1=AOA2=0° there may be coupling effects between thetwo users (due to the proximity of their antennas) that may vary theirperformance from the simulated results in FIG. 35.

iii. Preferred Scenario for DIDO 8×8

FIG. 24 illustrated that DIDO 8×8 yields a larger SE than lower orderDIDO at the expense of higher TX power requirement. The goal of thisanalysis is to show there are cases where DIDO 8×8 outperforms DIDO 2×2,not only in terms of peak spectral efficiency (SE), but also in terms ofTX power requirement (or TPT) to achieve that peak SE.

Note that, in i.i.d. (ideal) channels, there is ˜6 dB gap in TX powerbetween the SE of DIDO 8×8 and DIDO 2×2. This gap is due to the factthat DIDO 8×8 splits the TX power across eight data streams, whereasDIDO 2×2 only between two streams. This result is shown via simulationin FIG. 32.

In spatially correlated channels, however, the TPT is a function of thecharacteristics of the propagation environment (e.g., array orientation,user location, angle spread). For example, FIG. 35 shows ˜15 dB gap forlow angle spread between two different user device's locations. Similarresults are presented in FIG. 26 of the present application.

Similarly to MIMO systems, the performance of DIDO systems degrades whenthe users are located at endfire directions from the TX array (due tolack of diversity). This effect has been observed through measurementswith the current DIDO prototype. Hence, one way to show that DIDO 8×8outperforms DIDO 2×2 is to place the users at endfire directions withrespect to the DIDO 2×2 arrays. In this scenario, DIDO 8×8 outperformsDIDO 2×2 due to the higher diversity provided by the 8-antenna array.

In this analysis, consider the following systems:

System 1: DIDO 8×8 with 4-QAM (transmit 8 parallel data streams everytime slot);

System 2: DIDO 2×2 with 64-QAM (transmit to users X and Y every 4 timeslots). For this system we consider four combinations of TX and RXantenna locations: a) T1,T2 U1,2 (endfire direction); b) T3,T4 U3,4(endfire direction); c) T5,T6 U5,6 (˜30° from the endfire direction); d)T7,T8 U7,8 (NLOS (Non-Line of Sight));

System 3: DIDO 8×8 with 64-QAM; and

System 4: MISO 8×1 with 64-QAM (transmit to user X every 8 time slots).

For all these cases, an FEC rate of ¾ was used.

The users' locations are depicted in FIG. 27.

In FIG. 28 the SER results show a ˜15 dB gap between Systems 2 a and 2 cdue to different array orientations and user locations (similar to thesimulation results in FIG. 35). The first subplot in the second rowshows the values of TX power for which the SE curves saturate (i.e.corresponding to BER 1e-4). We observe that System 1 yields largerper-user SE for lower TX power requirement (˜5 dB less) than System 2.Also, the benefits of DIDO 8×8 versus DIDO 2×2 are more evident for theDL (downlink) SE and DL goodput due to multiplexing gain of DIDO 8×8over DIDO 2×2. System 4 has lower TX power requirement (8 dB less) thanSystem 1, due to the array gain of beamforming (i.e., MRC with MISO8×1). But System 4 yields only ⅓ of per-user SE compared to System 1.System 2 performs worse than System 1 (i.e., yields lower SE for largerTX power requirement). Finally, System 3 yields much larger SE (due tolarger order modulations) than System 1 for larger TX power requirement(˜15 dB).

From these results, the following conclusions may be drawn:

One channel scenario was identified for which DIDO 8×8 outperforms DIDO2×2 (i.e., yields larger SE for lower TX power requirement);

In this channel scenario, DIDO 8×8 yields larger per user SE and DL SEthan DIDO 2×2 and MISO 8×1; and

It is possible to further increase the performance of DIDO 8×8 by usinghigher order modulations (i.e., 64-QAM rather than 4-QAM) at the expenseof larger TX power requirements (˜15 dB more).

iv. DIDO with Antenna Selection

Hereafter, we evaluate the benefit of the antenna selection algorithmdescribed in R. Chen, R. W. Heath, and J. G. Andrews, “Transmitselection diversity for unitary precoded multiuser spatial multiplexingsystems with linear receivers,” accepted to IEEE Trans. on SignalProcessing, 2005. We present the results for one particular DIDO systemwith two users, 4-QAM and FEC rate of ½. The following systems arecompared in FIG. 27:

DIDO 2×2 with T1,2 and U1,2; and

DIDO 3×2 using antenna selection with T1,2,3 and U1,2.

The transmit antenna's and user device locations are the same as in FIG.27.

FIG. 29 shows that DIDO 3×2 with antenna selection may provide ˜5 dBgain compared to DIDO 2×2 systems (with no selection). Note that thechannel is almost static (i.e., no Doppler), so the selection algorithmsadapts to the path-loss and channel spatial correlation rather than thefast-fading. We should be seeing different gains in scenarios with highDoppler. Also, in this particular experiment it was observed that theantenna selection algorithm selects antennas 2 and 3 for transmission.

iv. SNR Thresholds for the LUTs

In section [0171] we stated that the mode selection is enabled by LUTs.The LUTs can be pre-computed by evaluating the SNR thresholds to achievecertain predefined target error-rate performance for the DIDOtransmission modes in different propagation environments. Hereafter, weprovide the performance of DIDO systems with and without antennaselection and variable number of users that can be used as guidelines toconstruct the LUTs. While FIGS. 24, 26, 28, 29 were derived frompractical measurements with the DIDO prototype, the following Figuresare obtained through simulations. The following BER results assume noFEC.

FIG. 30 shows the average BER performance of different DIDO precodingschemes in i.i.d. channels. The curve labeled as ‘no selection’ refersto the case when BD is employed. In the same figure the performance ofantenna selection (ASel) is shown for different number of extra antennas(with respect to the number of users). It is possible to see that as thenumber of extra antennas increases, ASel provides better diversity gain(characterized by the slope of the BER curve in high SNR regime),resulting in better coverage. For example, if we fix the target BER to10⁻² (practical value for uncoded systems), the SNR gain provided byASel increases with the number of antennas.

FIG. 31 shows the SNR gain of ASel as a function of the number of extratransmit antennas in i.i.d. channels, for different targets BER. It ispossible to see that, just by adding 1 or 2 antennas, ASel yieldssignificant SNR gains compared to BD. In the following sections, we willevaluate the performance of ASel only for the cases of 1 or 2 extraantennas and by fixing the target BER to 10⁻² (for uncoded systems).

FIG. 32 depicts the SNR thresholds as a function of the number of users(M) for BD and ASel with 1 and 2 extra antennas in i.i.d. channels. Weobserve that the SNR thresholds increase with M due to the largerreceive SNR requirement for larger number of users. Note that we assumefixed total transmit power (with variable number of transmit antennas)for any number of users. Moreover, FIG. 32 shows that the gain due toantenna selection is constant for any number of users in i.i.d.channels.

Hereafter, we show the performance of DIDO systems in spatiallycorrelated channels. We simulate each user's channel through theCOST-259 spatial channel model described in X. Zhuang, F. W. Vook, K. L.Baum, T. A. Thomas, and M. Cudak, “Channel models for link and systemlevel simulations,” IEEE 802.16 Broadband Wireless Access Working Group,September 2004. We generate single-cluster for each user. As a casestudy, we assume NLOS channels, uniform linear array (ULA) at thetransmitter, with element spacing of 0.5 lambda. For the case of 2-usersystem, we simulate the clusters with mean angles of arrival AOA1 andAOA2 for the first and second user, respectively. The AOAs are measuredwith respect to the broadside direction of the ULA. When more than twousers are in the system, we generate the users' clusters with uniformlyspaced mean AOAs in the range [−φ_(m),φ_(m)], where we define$\begin{matrix}{\Phi_{M} = \frac{\Delta\quad{\phi\left( {M - 1} \right)}}{2}} & (13)\end{matrix}$

with K being the number of users and Δφ is the angular separationbetween the users' mean AOAs. Note that the angular range [−φ_(m),φ_(m)]is centered at the 0° angle, corresponding to the broadside direction ofthe ULA. Hereafter, we study the BER performance of DIDO systems as afunction of the channel angle spread (AS) and angular separation betweenusers, with BD and ASel transmission schemes and different numbers ofusers.

FIG. 33 depicts the BER versus per-user average SNR for two userslocated at the same angular direction (i.e., AOA1=AOA2=0°, with respectto the broadside direction of the ULA), with different values of AS. Itis possible to see that as the AS increases the BER performance improvesand approaches the i.i.d. case. In fact, higher AS yields statisticallyless overlapping between the eigenmodes of the two users and betterperformance of the BD precoder.

FIG. 34 shows similar results as FIG. 33, but with higher angularseparation between the users. We consider AOA1=0° and AOA2=90° (i.e.,90° angular separation). The best performance is now achieved in the lowAS case. In fact, for the case of high angle separation, there is lessoverlapping between the users' eigenmodes when the angular spread islow. Interestingly, we observe that the BER performance in low AS isbetter than i.i.d. channels for the same reasons just mentioned.

Next, we compute the SNR thresholds, for target BER of 10⁻² in differentcorrelation scenarios. FIG. 35 plots the SNR thresholds as a function ofthe AS for different values of the mean AOAs of the users. For lowusers' angular separation reliable transmissions with reasonable SNRrequirement (i.e., 18 dB) are possible only for channels characterizedby high AS. On the other hand, when the users are spatially separated,less SNR is required to meet the same target BER.

FIG. 36 shows the SNR threshold for the case of five users. The users'mean AOAs are generated according to the definition in (13), withdifferent values of angular separation Δφ. We observe that for Δφ=0° andAS<15°, BD performs poorly due to the small angle separation betweenusers, and the target BER is not satisfied. For increasing AS the SNRrequirement to meet the fixed target BER decreases. On the other end,for Δφ=30°, the smallest SNR requirement is obtained at low AS,consistently to the results in FIG. 35. As the AS increases, the SNRthresholds saturate to the one of i.i.d. channels. Note that_(—)Δφ=30°with 5 users corresponds to the AOA range of [−60°, 60°], that istypical for base stations in cellular systems with 120° sectorizedcells.

Next, we study the performance of ASel transmission scheme in spatiallycorrelated channels. FIG. 37 compares the SNR threshold of BD and ASel,with 1 and 2 extra antennas, for two user case. We consider twodifferent cases of angular separation between users: {AOA1=0°,AOA2=0°}and {AOA1=0°,AOA2=90°}. The curves for BD scheme (i.e., no antennaselection) are the same as in FIG. 35. We observe that ASel yields 8 dBand 10 dB SNR gains with 1 and 2 extra antennas, respectively, for highAS. As the AS decreases, the gain due to ASel over BD becomes smallerdue to the reduced number of degrees of freedom in the MIMO broadcastchannel. Interestingly, for AS=0° (i.e., close to LOS channels) and thecase {AOA1=0°,AOA2=90°}, ASel does not provide any gain due to the luckof diversity in the space domain. FIG. 38 shows similar results as FIG.37, but for five user case.

We compute the SNR thresholds (assuming usual target BER of 10⁻²) as afunction of the number of users in the system (M), for both BD and ASeltransmission schemes. The SNR thresholds correspond to the average SNR,such that the total transmit power is constant for any M. We assumemaximum separation between the mean AOAs of each user's cluster withinthe azimuth range [−φ_(m),φ_(m)]=[−60°, 60°]. Then, the angularseparation between users is Δφ=120°/(M−1).

FIG. 39 shows the SNR thresholds for BD scheme with different values ofAS. We observe that the lowest SNR requirement is obtained for AS=0.1°(i.e., low angle spread) with relatively small number of users (i.e.,K≦20), due to the large angular separation between users. For M>50,however, the SNR requirement is way above 40 dB, since Δφ is very small,and BD is impractical. Moreover, for AS>10° the SNR thresholds remainalmost constant for any M, and the DIDO system in spatially correlatedchannels approaches the performance of i.i.d. channels.

To reduce the values of the SNR thresholds and improve the performanceof the DIDO system we apply ASel transmission scheme. FIG. 40 depictsthe SNR thresholds in spatially correlated channels with AS=0.1° for BDand ASel with 1 and 2 extra antennas. For reference we report also thecurves for the i.i.d. case shown in FIG. 32. It is possible to see that,for low number of users (i.e., M≦10), antenna selection does not helpreducing the SNR requirement due to the lack of diversity in the DIDObroadcast channel. As the number of users increases, ASel benefits frommultiuser diversity yielding SNR gains (i.e., 4 dB for M=20). Moreover,for M≦20, the performance of ASel with 1 or 2 extra antennas in highlyspatially correlated channels is the same.

We then compute the SNR thresholds for two more channel scenarios: AS=5°in FIG. 41 and AS=10° in FIG. 42. FIG. 41 shows that ASel yields SNRgains also for relatively small number of users (i.e., M≦10) as opposedto FIG. 40, due to the larger angle spread. For AS=10° the SNRthresholds reduce further and the gains due to ASel get higher, asreported in FIG. 42.

Finally, we summarize the results presented so far for correlatedchannels. FIG. 43 and FIG. 44 show the SNR thresholds as a function ofthe number of users (M) and angle spread (AS) for BD and ASel schemes,with 1 and 2 extra antennas, respectively. Note that the case of AS=30°corresponds actually to i.i.d. channels, and we used this value of AS inthe plot only for graphical representation. We observe that, while BD isaffected by the channel spatial correlation, ASel yields almost the sameperformance for any AS. Moreover, for AS=0.1°, ASel performs similarlyto BD for low M, whereas outperforms BD for large M (i.e., M≧20), due tomultiuser diversity.

FIG. 49 compares the performance of different DIDO schemes in terms ofSNR thresholds. The DIDO schemes considered are: BD, ASel, BD witheigenmode selection (BD-ESel) and maximum ratio combining (MRC). Notethat MRC, does not pre-cancel interference at the transmitter (unlikethe other methods), but does provide larger gain in case the users arespatially separated. In FIG. 49 we plot the SNR threshold for targetBER=10−2 for DIDO N×2 systems when the two users are located at −30° and30° from the broadside direction of the transmit array, respectively. Weobserve that for low AS the MRC scheme provides 3 dB gain compared tothe other schemes since the users' spatial channels are well separatedand the effect of inter-user interference is low. Note that the gain ofMRC over DIDO N×2 are due to array gain. For AS larger than 20° theQR-ASel scheme outperforms the other and yields about 10 dB gaincompared to BD 2×2 with no selection. QR-ASel and BD-ESel provide aboutthe same performance for any value of AS.

Described above is a novel adaptive transmission technique for DIDOsystems. This method dynamically switches between DIDO transmissionmodes to different users to enhance throughput for fixed target errorrate. The performance of different order DIDO systems was measured indifferent propagation conditions and it was observed that significantgains in throughput may be achieved by dynamically selecting the DIDOmodes and number of users as a function of the propagation conditions.

III. Pre-compensation of Frequency and Phase Offset

a. Background

As previously described, wireless communication systems use carrierwaves to convey information. These carrier waves are usually sinusoidsthat are amplitude and/or phase modulated in response to information tobe transmitted. The nominal frequency of the sinusoid is known as thecarrier frequency. To create this waveform, the transmitter synthesizesone or more sinusoids and uses upconversion to create a modulated signalriding on a sinusoid with the prescribed carrier frequency. This may bedone through direct conversion where the signal is directly modulated onthe carrier or through multiple upconversion stages. To process thiswaveform, the receiver must demodulate the received RF signal andeffectively remove the modulating carrier. This requires that thereceiver synthesize one or more sinusoidal signals to reverse theprocess of modulation at the transmitter, known as downconversion.Unfortunately, the sinusoidal signals generated at the transmitter andreceiver are derived from different reference oscillators. No referenceoscillator creates a perfect frequency reference; in practice there isalways some deviation from the true frequency.

In wireless communication systems, the differences in the outputs of thereference oscillators at the transmitter and receivers create thephenomena known as carrier frequency offset, or simply frequency offset,at the receiver. Essentially there is some residual modulation in thereceived signal (corresponding to the difference in the transmit andreceive carriers), which occurs after downconversion. This createsdistortion in the received signal resulting in higher bit error ratesand lower throughput.

There are different techniques for dealing with carrier frequencyoffset. Most approaches estimate the carrier frequency offset at thereceiver then apply a carrier frequency offset correction algorithm. Thecarrier frequency offset estimation algorithm may be blind using offsetQAM (T. Fusco and M. Tanda, “Blind Frequency-offset Estimation forOFDM/OQAM Systems,” Signal Processing, IEEE Transactions on [see alsoAcoustics, Speech, and Signal Processing, IEEE Transactions on], vol.55, pp. 1828-1838, 2007); periodic properties (E. Serpedin, A.Chevreuil, G. B. Giannakis, and P. Loubaton, “Blind channel and carrierfrequency offset estimation using periodic modulation precoders,” SignalProcessing, IEEE Transactions on [see also Acoustics, Speech, and SignalProcessing, IEEE Transactions on], vol. 48, no. 8, pp. 2389-2405, August2000); or the cyclic prefix in orthogonal frequency divisionmultiplexing (OFDM) structure approaches (J. J. van de Beek, M. Sandell,and P. O. Borjesson, “ML estimation of time and frequency offset in OFDMsystems,” Signal Processing, IEEE Transactions on [see also Acoustics,Speech, and Signal Processing, IEEE Transactions on], vol. 45, no. 7,pp. 1800-1805, July 1997; U. Tureli, H. Liu, and M. D. Zoltowski, “OFDMblind carrier offset estimation: ESPRIT,” IEEE Trans. Commun., vol. 48,no. 9, pp. 1459-1461, September 2000; M. Luise, M. Marselli, and R.Reggiannini, “Low-complexity blind carrier frequency recovery for OFDMsignals over frequency-selective radio channels,” IEEE Trans. Commun.,vol. 50, no. 7, pp. 1182-1188, July 2002).

Alternatively special training signals may be utilized including arepeated data symbol (P. H. Moose, “A technique for orthogonal frequencydivision multiplexing frequency offset correction,” IEEE Trans. Commun.,vol. 42, no. 10, pp. 2908-2914, Oct. 1994); two different symbols (T. M.Schmidl and D. C. Cox, “Robust frequency and timing synchronization forOFDM,” IEEE Trans. Commun., vol. 45, no. 12, pp. 1613-1621, Dec. 1997);or periodically inserted known symbol sequences (M. Luise and R.Reggiannini, “Carrier frequency acquisition and tracking for OFDMsystems,” IEEE Trans. Commun., vol. 44, no. 11, pp. 1590-1598, Nov.1996). The correction may occur in analog or in digital. The receivercan also use carrier frequency offset estimation to precorrect thetransmitted signal to eliminate offset. Carrier frequency offsetcorrection has been studied extensively for multicarrier and OFDMsystems due to their sensitivity to frequency offset (J. J. van de Beek,M. Sandell, and P. O. Borjesson, “ML estimation of time and frequencyoffset in OFDM systems,” Signal Processing, IEEE Transactions on [seealso Acoustics, Speech, and Signal Processing, IEEE Transactions on],vol. 45, no. 7, pp. 1800-1805, July 1997; U. Tureli, H. Liu, and M. D.Zoltowski, “OFDM blind carrier offset estimation: ESPRIT,” IEEE Trans.Commun., vol. 48, no. 9, pp. 1459-1461, September 2000; T. M. Schmidland D. C. Cox, “Robust frequency and timing synchronization for OFDM,”IEEE Trans. Commun., vol. 45, no. 12, pp. 1613-1621, Dec. 1997; M.Luise, M. Marselli, and R. Reggiannini, “Low-complexity blind carrierfrequency recovery for OFDM signals over frequency-selective radiochannels,” IEEE Trans. Commun., vol. 50, no. 7, pp. 1182-1188, July2002).

Frequency offset estimation and correction is an important issue formulti-antenna communication systems, or more generally MIMO (multipleinput multiple output) systems. In MIMO systems where the transmitantennas are locked to one frequency reference and the receivers arelocked to another frequency reference, there is a single offset betweenthe transmitter and receiver. Several algorithms have been proposed totackle this problem using training signals (K. Lee and J. Chun,“Frequency-offset estimation for MIMO and OFDM systems using orthogonaltraining sequences,” IEEE Trans. Veh. Technol., vol. 56, no. 1, pp.146-156, January 2007; M. Ghogho and A. Swami, “Training design formultipath channel and frequency offset estimation in MIMO systems,”Signal Processing, IEEE Transactions on [see also Acoustics, Speech, andSignal Processing, IEEE Transactions on], vol. 54, no. 10, pp.3957-3965, October 2006, and adaptive tracking C. Oberli and B.Daneshrad, “Maximum likelihood tracking algorithms for MIMOOFDM,” inCommunications, 2004 IEEE International Conference on, vol. 4, Jun.20-24, 2004, pp. 2468-2472). A more severe problem is encountered inMIMO systems where the transmit antennas are not locked to the samefrequency reference but the receive antennas are locked together. Thishappens practically in the uplink of a spatial division multiple access(SDMA) system, which can be viewed as a MIMO system where the differentusers correspond to different transmit antennas. In this case thecompensation of frequency offset is much more complicated. Specifically,the frequency offset creates interference between the differenttransmitted MIMO streams. It can be corrected using complex jointestimation and equalization algorithms (A. Kannan, T. P. Krauss, and M.D. Zoltowski, “Separation of cochannel signals under imperfect timingand carrier synchronization,” IEEE Trans. Veh. Technol., vol. 50, no. 1,pp. 79-96, January 2001), and equalization followed by frequency offsetestimation (T. Tang and R. W. Heath, “Joint frequency offset estimationand interference cancellation for MIMO-OFDM systems [mobile radio],”2004. VTC2004-Fall. 2004 IEEE 60^(th) Vehicular Technology Conference,vol. 3, pp. 1553-1557, Sep. 26-29, 2004; X. Dai, “Carrier frequencyoffset estimation for OFDM/SDMA systems using consecutive pilots,” IEEEProceedings—Communications, vol. 152, pp. 624-632, Oct. 7, 2005). Somework has dealt with the related problem of residual phase off-set andtracking error, where residual phase offsets are estimated andcompensated after frequency offset estimation, but this work onlyconsider the uplink of an SDMA OFDMA system (L. Haring, S. Bieder, andA. Czylwik, “Residual carrier and sampling frequency synchronization inmultiuser OFDM systems,” 2006. VTC 2006-Spring. IEEE 63rd VehicularTechnology Conference, vol. 4, pp. 1937-1941, 2006). The most severecase in MIMO systems occurs when all transmit and receive antennas havedifferent frequency references. The only available work on this topiconly deals with asymptotic analysis of estimation error in flat fadingchannels (O. Besson and P. Stoica, “On parameter estimation of MIMOflat-fading channels with frequency offsets,” Signal Processing, IEEETransactions on [see also Acoustics, Speech, and Signal Processing, IEEETransactions on], vol. 51, no. 3, pp. 602-613, March 2003).

A case that has not been significantly investigated occurs when thedifferent transmit antennas of a MIMO system do not have the samefrequency reference and the receive antennas process the signalsindependently. This happens in what is known as a distributed inputdistributed-output (DIDO) communication system, also called the MIMObroadcast channel in the literature. DIDO systems consist of one accesspoint with distributed antennas that transmit parallel data streams (viapreceding) to multiple users to enhance downlink throughput, whileexploiting the same wireless resources (i.e., same slot duration andfrequency band) as conventional SISO systems. Detailed description ofDIDO systems was presented in, S. G. Perlman and T. Cotter, “System andmethod for distributed input-distributed output wirelesscommunications,” United States Patent Application 20060023803, July2004. There are many ways to implement DIDO precoders. One solution isblock diagonalization (BD) described in, for example, Q. H. Spencer, A.L. Swindlehurst, and M. Haardt, “Zero-forcing methods for downlinkspatial multiplexing in multiuser MIMO channels,” IEEE Trans. Sig.Proc., vol. 52, pp. 461-471, February 2004; K. K. Wong, R. D. Murch, andK. B. Letaief, “A joint-channel diagonalization for multiuser MIMOantenna systems,” IEEE Trans. Wireless Comm., vol. 2, pp. 773-786, July2003; L. U. Choi and R. D. Murch, “A transmit preprocessing techniquefor multiuser MIMO systems using a decomposition approach,” IEEE Trans.Wireless Comm., vol. 3, pp. 20-24, January 2004; Z. Shen, J. G. Andrews,R. W. Heath, and B. L. Evans, “Low complexity user selection algorithmsfor multiuser MIMO systems with block diagonalization,” accepted forpublication in IEEE Trans. Sig. Proc., September 2005; Z. Shen, R. Chen,J. G. Andrews, R. W. Heath, and B. L. Evans, “Sum capacity of multiuserMIMO broadcast channels with block diagonalization,” submitted to IEEETrans. Wireless Comm., October 2005; R. Chen, R. W. Heath, and J. G.Andrews, “Transmit selection diversity for unitary precoded multiuserspatial multiplexing systems with linear receivers,” accepted to IEEETrans. on Signal Processing, 2005.

In DIDO systems, transmit precoding is used to separate data streamsintended for different users. Carrier frequency offset causes severalproblems related to the system implementation when the transmit antennaradio frequency chains do not share the same frequency reference. Whenthis happens, each antenna is effectively transmits at a slightlydifferent carrier frequency. This destroys the integrity of the DIDOprecoder resulting in each user experiencing extra interference. Proposebelow are several solutions to this problem. In one embodiment of thesolution, the DIDO transmit antennas share a frequency reference througha wired, optical, or wireless network. In another embodiment of thesolution, one or more users estimate the frequency offset differences(the relative differences in the offsets between pairs of antennas) andsend this information back to the transmitter. The transmitter thenprecorrects for the frequency offset and proceeds with the training andprecoder estimation phase for DIDO. There is a problem with thisembodiment when there are delays in the feedback channel. The reason isthat there may be residual phase errors created by the correctionprocess that are not accounted for in the subsequent channel estimation.To solve this problem, one additional embodiment uses a novel frequencyoffset and phase estimator that can correct this problem by estimatingthe delay. Results are presented based both on simulations and practicalmeasurements carried out with a DIDO-OFDM prototype.

The frequency and phase offset compensation method proposed in thisdocument may be sensitive to estimation errors due to noise at thereceiver. Hence, one additional embodiment proposes methods for time andfrequency offset estimation that are robust also under low SNRconditions.

There are different approaches for performing time and frequency offsetestimation. Because of its sensitivity to synchronization errors, manyof these approaches were proposed specifically for the OFDM waverform.

The algorithms typically do not exploit the structure of the OFDMwaveform thus they are generic enough for both single carrier andmulticarrier waveforms. The algorithm described below is among a classof techniques that employ known reference symbols, e.g. training data,to aid in synchronization. Most of these methods are extensions ofMoose's frequency offset estimator (see P. H. Moose, “A technique fororthogonal frequency division multiplexing frequency offset correction,”IEEE Trans. Commun., vol. 42, no. 10, pp. 2908-2914, October 1994.).Moose proposed to use two repeated training signals and derived thefrequency offset using the phase difference between both receivedsignals. Moose's method can only correct for the fractional frequencyoffset. An extension of the Moose method was proposed by Schmidl and Cox(T. M. Schmidl and D. C. Cox, “Robust frequency and timingsynchronization for OFDM,” IEEE Trans. Commun., vol. 45, no. 12, pp.1613-1621, Dec. 1997.). Their key innovation was to use one periodicOFDM symbol along with an additional differentially encoded trainingsymbol. The differential encoding in the second symbol enables integeroffset correction. Coulson considered a similar setup as described in T.M. Schmidl and D. C. Cox, “Robust frequency and timing synchronizationfor OFDM,” IEEE Trans. Commun., vol. 45, no. 12, pp. 1613-1621, Dec.1997, and provided a detailed discussion of algorithms and analysis asdescribed in A. J. Coulson, “Maximum likelihood synchronization for OFDMusing a pilot symbol: analysis,” IEEE J. Select. Areas Commun., vol. 19,no. 12, pp. 2495-2503, December 2001.; A. J. Coulson, “Maximumlikelihood synchronization for OFDM using a pilot symbol: algorithms,”IEEE J. Select. Areas Commun., vol. 19, no. 12, pp. 2486-2494, December2001. One main difference is that Coulson uses repeated maximum lengthsequences to provide good correlation properties. He also suggests usingchirp signals because of their constant envelope properties in the timeand frequency domains. Coulson considers several practical details butdoes not include integer estimation. Multiple repeated training signalswere considered by Minn et. al. in H. Minn, V. K. Bhargava, and K. B.Letaief, “A robust timing and frequency synchronization for OFDMsystems,” IEEE Trans. Wireless Commun., vol. 2, no. 4, pp. 822-839, July2003, but the structure of the training was not optimized. Shi andSerpedin show that the training structure has some optimality form theperspective of frame synchronization (K. Shi and E. Serpedin, “Coarseframe and carrier synchronization of OFDM systems: a new metric andcomparison,” IEEE Trans. Wireless Commun., vol. 3, no. 4, pp. 1271-1284,July 2004). One embodiment of the invention uses the Shi and Serpedinapproach to perform frame synchronization and fractional frequencyoffset estimation.

Many approaches in the literature focus on frame synchronization andfractional frequency offset correction. Integer offset correction issolved using an additional training symbol as in T. M. Schmidl and D. C.Cox, “Robust frequency and timing synchronization for OFDM,” IEEE Trans.Commun., vol. 45, no. 12, pp. 1613-1621, Dec. 1997. For example,Morrelli et. al. derived an improved version of T. M. Schmidl and D. C.Cox, “Robust frequency and timing synchronization for OFDM,” IEEE Trans.Commun., vol. 45, no. 12, pp. 1613-1621, Dec. 1997, in M. Morelli, A. N.D'Andrea, and U. Mengali, “Frequency ambiguity resolution in OFDMsystems,” IEEE Commun. Lett., vol. 4, no. 4, pp. 134-136, April 2000. Analternative approach using a different preamble structure was suggestedby Morelli and Mengali (M. Morelli and U. Mengali, “An improvedfrequency offset estimator for OFDM applications,” IEEE Commun. Lett.,vol. 3, no. 3, pp. 75-77, Mar. 1999). This approach uses thecorrelations between M repeated identical training symbols to increasethe range of the fractional frequency offset estimator by a factor of M.This is the best linear unbiased estimator and accepts a large offset(with proper design) but does not provide good timing synchronization.

System Description

One embodiment of the invention uses pre-coding based on channel stateinformation to cancel frequency and phase offsets in DIDO systems. SeeFIG. 11 and the associated description above for a description of thisembodiment.

In one embodiment of the invention, each user employs a receiverequipped with frequency offset estimator/compensator. As illustrated inFIG. 45, in one embodiment of the invention, a system including thereceiver includes a plurality of RF units 4508, a correspondingplurality of A/D units 4510, a receiver equipped with a frequency offsetestimator/compensator 4512 and a DIDO feedback generator unit 4506.

The RF units 4508 receive signals transmitted from the DIDO transmitterunits, downconvert the signals to baseband and provide the downconvertedsignals to the A/D units 4510. The A/D units 4510 then convert thesignal from analog to digital and send it to the frequency offsetestimator/compensator units 4512. The frequency offsetestimator/compensator units 4512 estimate the frequency offset andcompensate for it, as described herein, and then send the compensatedsignal to the OFDM units 4513. The OFDM units 4513 remove the cyclicprefix and operate the Fast Fourier Transform (FFT) to report the signalto the frequency domain. During the training period the OFDM units 4513send the output to the channel estimate unit 4504 that computes thechannel estimates in the frequency domain. Alternatively, the channelestimates can be computed in the time domain. During the data period theOFDM units 4513 send the output to the DIDO receiver unit 4502 whichdemodulates/decodes the signal to obtain the data. The channel estimateunit 4504 sends the channel estimates to the DIDO feedback generatorunit 4506 that may quantize the channel estimates and send them back tothe transmitter via the feedback control channel, as illustrated.

Description of One Embodiment of an Algorithm for a DIDO 2×2 Scenario

Described below are embodiments of an algorithm for frequency/phaseoffset compensation in DIDO systems. The DIDO system model is initiallydescribed with and without frequency/phase offsets. For the sake of thesimplicity, the particular implementation of a DIDO 2×2 system isprovided. However, the underlying principles of the invention may alsobe implemented on higher order DIDO systems.

DIDO System Model w/o Frequency and Phase Offset

The received signals of DIDO 2×2 can be written for the first user asr ₁ [t]=h ₁₁(w ₁₁ x ₁ [t]+w ₂₁ x ₂ [t])+h ₁₂(w ₁₂ x ₁ [t]+w ₂₂ x ₂[t])  (1)and for the second user asr ₂ [t]=h ₂₁(w ₁₁ x ₁ [t]+w ₂₁ x ₂ [t])+h ₂₂(w ₁₂ x ₁ [t]+w ₂₂ x ₂[t])  (2)where t is the discrete time index, h_(mn) hand w_(mn) are the channeland the DIDO preceding weights between the m-th user and n-th transmitantenna, respectively, and x_(m), is the transmit signal to user m. Notethat h_(mn) and w_(mn) are not a function of t since we assume thechannel is constant over the period between training and datatransmission.

In the presence of frequency and phase offset, the received signals areexpressed asr ₁ [t]=e ^(j(ω) ^(U1) ^(−ω) ^(T1) ^()T) ^(s) ^((t−t) ¹¹ ⁾ h ₁₁(w ₁₁ x ₁[t]+w ₂₁ x ₂ [t])+e ^(j(ω) ^(U1) ^(−ω) ^(T2) ^()T) ^(s) ^((t−t) ¹² ⁾ h₁₂(w ₁₂ x ₁ [t]+w ₂₂ x ₂ [t])  (3)andr ₂ [t]=e ^(j(ω) ^(U2) ^(−ω) ^(T1) ^()T) ^(s) ^((t−t) ²¹ ⁾ h ₂₁(w ₁₁ x ₁[t]+w ₂₁ x ₂ [t])+e ^(j(ω) ^(U2) ^(−ω) ^(T2) ^()T) ^(s) ^((t−t) ²² ⁾ h₂₂(w ₁₂ x ₁ [t]+w ₂₂ x ₂ [t])  (4)where T_(s) is the symbol period, ω_(Tn)=2πf_(Tn) for the n-th transmitantenna, ω_(Um)=2πf_(Um) for the m-th user, and f_(Tn) and f_(Um) arethe actual carrier frequencies (affected by offset) for the n-thtransmit antenna and m-th user, respectively. The values t_(mn) denoterandom delays that cause phase offset over the channel h_(mn). FIG. 46depicts the DIDO 2×2 system model.

For the time being, we use the following definitions:Δω_(mn)=ω_(Um)−ω_(Tn)  (5)to denote the frequency offset between the m-th user and the n-thtransmit antenna.

Description of One Embodiment of the Invention

A method according to one embodiment of the invention is illustrated inFIG. 47. The method includes the following general steps (which includesub-steps, as illustrated): training period for frequency offsetestimation 4701; training period for channel estimation 4702; datatransmission via DIDO precoding with compensation 4703. These steps aredescribed in detail below.

(a) Training Period for Frequency Offset Estimation (4701)

During the first training period the base station sends one or moretraining sequences from each transmit antennas to one of the users (4701a). As described herein “users” are wireless client devices. For theDIDO 2×2 case, the signal received by the m-th user is given byr _(m) [t]=e ^(jΔω) ^(m1) ^(T) ^(s) ^((t−t) ^(m1) ⁾ h _(m1) p ₁ [t]+e^(jΔω) ^(m2) ^(T) ^(s) ^((t−t) ^(m2) ⁾ h _(m2) p ₂ [t]  (6)where p₁ and p₂ are the training sequences transmitted from the firstand second antennas, respectively.

The m-th user may employ any type of frequency offset estimator (i.e.,convolution by the training sequences) and estimates the offsets Δω_(m1)and Δω_(m2). Then, from these values the user computes the frequencyoffset between the two transmit antennas asΔω_(T)=Δω_(m2)−Δω_(m1)=ω_(T1)−ω_(T2)  (7)Finally, the value in (7) is fed back to the base station (4701 b).

Note that p₁ and p₂ in (6) are designed to be orthogonal, so that theuser can estimate Δω_(m1) and Δω_(m2). Alternatively, in one embodiment,the same training sequence is used over two consecutive time slots andthe user estimates the offset from there. Moreover, to improve theestimate of the offset in (7) the same computations described above canbe done for all users of the DIDO systems (not just for the m-th user)and the final estimate may be the (weighted) average of the valuesobtained from all users. This solution, however, requires morecomputational time and amount of feedback. Finally, updates of thefrequency offset estimation are needed only if the frequency offsetvaries over time. Hence, depending on the stability of the clocks at thetransmitter, this step 4701 of the algorithm can be carried out on along-term basis (i.e., not for every data transmission), resulting inreduction of feedback overhead.

(b) Training Period for Channel Estimation (4702)

During the second training period, the base station first obtains thefrequency offset feedback with the value in (7) from the m-th user orfrom the plurality of users. The value in (7) is used to pre-compensatefor the frequency offset at the transmit side. Then, the base stationsends training data to all the users for channel estimation (4702 a).

For DIDO 2×2 systems, the signal received at the first user is given byr ₁ [t]=e ^(jΔω) ¹¹ ^(T) ^(s) ^((t−{tilde over (t)}) ¹¹ ⁾ h ₁₁ p ₁ [t]+e^(jΔω) ¹² ^(T) ^(s) ^((t−{tilde over (t)}) ¹² ⁾ h ₁₂ e ^(−jΔω) ^(T) ^(T)^(s) ^(t) p ₂ [t]  (8)and at the second user byr ₂ [t]=e ^(jΔω) ²¹ ^(T) ^(s) ^((t−{tilde over (t)}) ²¹ ⁾ h ₂₁ p ₁ [t]+e^(jΔω) ²² ^(T) ^(s) ^((t−{tilde over (t)}) ²² ⁾ h ₂₂ e ^(−jΔω) ^(T) ^(T)^(s) ^(t) p ₂ [t]  (9)where {tilde over (t)}_(mn)=t_(mn)+Δt and Δt is random or known delaybetween the first and second transmissions of the base station.Moreover, p₁ and p₂ are the training sequences transmitted from thefirst and second antennas, respectively, for frequency offset andchannel estimation.

Note that the pre-compensation is applied only to the second antennas inthis embodiment.

Expanding (8) we obtainr ₁ [t]=e ^(jΔω) ¹¹ ^(T) ^(s) ^(t) e ^(jθ) ¹¹ [h ₁₁ p ₁ [t]+e ^(j(θ) ¹²^(−θ) ¹¹ ⁾ h ₁₂ p ₂ [t]]  (10)and similarly for the second userr ₂ [t]=e ^(jΔω) ²¹ ^(T) ^(s) ^(t) e ^(jθ) ²¹ [h ₂₁ p ₁ [t]+e ^(j(θ) ²²^(−θ) ²¹ ⁾ h ₂₂ p ₂ [t]]  (11)where θ_(mn)=−Δω_(mn)T_(s){tilde over (t)}_(mn).

At the receive side, the users compensate for the residual frequencyoffset by using the training sequences p₁ and p₂. Then the usersestimate via training the vector channels (4702 b) $\begin{matrix}{{h_{1} = \begin{bmatrix}h_{11} \\{{\mathbb{e}}^{j{({\theta_{12} - \theta_{11}})}}h_{12}}\end{bmatrix}}{h_{2} = \begin{bmatrix}h_{21} \\{{\mathbb{e}}^{j{({\theta_{22} - \theta_{21}})}}h_{22}}\end{bmatrix}}} & (12)\end{matrix}$

These channel in (12) or channel state information (CSI) is fed back tothe base station (4702 b) that computes the DIDO precoder as describedin the following subsection.

(c) DIDO Precoding with Pre-Compensation (4703)

The base station receives the channel state information (CSI) in (12)from the users and computes the precoding weights via blockdiagonalization (BD) (4703 a), such thatw₁ ^(T)h₂=0, w₂ ^(T)h₁=0  (13)where the vectors h_(I) are defined in (12) and w_(m)=[w_(m1),w_(m2)].Note that the invention presented in this disclosure can be applied toany other DIDO precoding method besides BD. The base station alsopre-compensates for the frequency offset by employing the estimate in(7) and phase offset by estimating the delay (Δt_(o)) between the secondtraining transmission and the current transmission (4703 a). Finally,the base station sends data to the users via the DIDO precoder (4703 b).

After this transmit processing, the signal received at user 1 is givenby $\begin{matrix}\begin{matrix}{{r_{1}\lbrack t\rbrack} = {{\mathbb{e}}^{j\quad\Delta\quad\omega_{11}{T_{s}{({t - {\overset{\sim}{t}}_{11} - {\Delta\quad t_{o}}})}}}{h_{11}\left\lbrack {{w_{11}{x_{1}\lbrack t\rbrack}} + {w_{21}{x_{2}\lbrack t\rbrack}}} \right\rbrack}}} \\{= {{\mathbb{e}}^{j\quad\Delta\quad\omega_{12}{T_{s}{({t - {\overset{\sim}{t}}_{12} - {\Delta\quad t_{o}}})}}}h_{12}{{\mathbb{e}}^{{- j}\quad\Delta\quad\omega_{T}{T_{s}{({t - {\Delta\quad t_{o}}})}}}\left\lbrack {{w_{12}{x_{1}\lbrack t\rbrack}} + {w_{22}{x_{2}\lbrack t\rbrack}}} \right\rbrack}}} \\{= {{\gamma_{1}\lbrack t\rbrack}\left\lfloor \begin{matrix}{{h_{11}\left( {{w_{11}{x_{1}\lbrack t\rbrack}} + {w_{21}{x_{2}\lbrack t\rbrack}}} \right)} +} \\{{\mathbb{e}}^{\quad{{j{({{\Delta\quad\omega_{11}\quad t_{11}}\quad - \quad{\Delta\quad\omega_{12}\quad t_{12}}})}}\quad T_{s}}}{h_{12}\left( {{w_{12}{x_{\quad 1}\lbrack t\rbrack}} + {w_{22}{x_{2}\lbrack t\rbrack}}} \right)}}\end{matrix} \right\rfloor}} \\{= {{\gamma_{1}\lbrack t\rbrack}\begin{bmatrix}{{\left( {{h_{11}w_{11}} + {{\mathbb{e}}^{j(\quad{\theta_{12}\quad - \quad\theta_{11}})}h_{12}w_{12}}} \right){x_{\quad 1}\lbrack t\rbrack}} +} \\{\left( {{h_{11}w_{21}} + {{\mathbb{e}}^{j\quad{(\quad{\theta_{12}\quad - \quad\theta_{11}})}}h_{12}w_{22}}} \right){x_{\quad 2}\lbrack t\rbrack}}\end{bmatrix}}}\end{matrix} & (14)\end{matrix}$where γ₁[t]=e^(jΔω) ¹¹ ^(T) ^(s) ^((t−{tilde over (t)}) ¹¹ ^(−Δt) ^(o)⁾. Using the property (13) we obtainr₁[t]=γ₁[t]w₁ ^(T)h₁x₁[t].  (15)Similarly, for user 2 we get $\begin{matrix}{{r_{2}\lbrack t\rbrack} = {{{\mathbb{e}}^{j\quad\Delta\quad\omega_{21}{T_{s}{({t - {\overset{\sim}{t}}_{21} - {\Delta\quad t_{o}}})}}}{h_{21}\left\lbrack {{w_{11}{x_{1}\lbrack t\rbrack}} + {w_{21}{x_{2}\lbrack t\rbrack}}} \right\rbrack}} + {{\mathbb{e}}^{j\quad\Delta\quad\omega_{22}\quad{T_{s}{({t\quad - \quad{\quad\overset{\sim}{t}}_{22}\quad - \quad{\Delta\quad t_{o}}})}}}h_{22}{{\mathbb{e}}^{{- j}\quad\Delta\quad\omega_{T}\quad{T_{s}{({t\quad - \quad{\Delta\quad t_{o}}})}}}\left\lbrack {{w_{21}{x_{1}\lbrack t\rbrack}} + {w_{22}{x_{2}\lbrack t\rbrack}}} \right\rbrack}}}} & (16)\end{matrix}$and expanding (16)r₂[t]=γ₂[t]w₂ ^(T)h₂x₂[t]  (17)

where γ₂[t]=e^(jΔω) ²¹ ^(T) ^(s) ^((t−{tilde over (t)}) ²¹ ^(−Δt) ^(o)⁾.

Finally, the users compute the residual frequency offset and the channelestimation to demodulate the data streams x₁[t] and x₂[t] (4703 c).

Generalization to DIDO N×M

In this section, the previously described techniques are generalized toDIDO systems with N transmit antennas and M users.

i. Training Period for Frequency Offset Estimation

During the first training period, the signal received by the m-th useras a result of the training sequences sent from the N antennas is givenby $\begin{matrix}{{r_{m}\lbrack t\rbrack} = {\sum\limits_{n = 1}^{N}{{\mathbb{e}}^{j\quad\Delta\quad\omega_{mn}{T_{s}{({t - t_{mn}})}}}h_{mn}{p_{n}\lbrack t\rbrack}}}} & (18)\end{matrix}$where p_(n) is the training sequences transmitted from the n-th antenna.

After estimating the offsets Δω_(mn), ∀n=1, . . . , N, the m-th usercomputes the frequency offset between the first and the n-th transmitantenna asΔω_(T,1n)=Δω_(mn)−Δω_(m1)=ω_(T1)−ω_(Tn).  (19)Finally, the values in (19) are fed back to the base station.

ii. Training Period for Channel Estimation

During the second training period, the base station first obtains thefrequency offset feedback with the value in (19) from the m-th user orfrom the plurality of users. The value in (19) is used to pre-compensatefor the frequency offset at the transmit side. Then, the base stationsends training data to all the users for channel estimation.

For DIDO N×M systems, the signal received at the m-th user is given by$\begin{matrix}\begin{matrix}{{r_{m}\lbrack t\rbrack} = {{{\mathbb{e}}^{j\quad\Delta\quad\omega_{m\quad 1}{T_{S}{({t - {\overset{\sim}{t}}_{m\quad 1}})}}}h_{m\quad 1}{p_{1}\lbrack t\rbrack}} +}} \\{\sum\limits_{n = 2}^{N}{{\mathbb{e}}^{j\quad\Delta\quad\omega_{mn}{T_{s}{({t - {\overset{\sim}{t}}_{mn}})}}}h_{mn}{\mathbb{e}}^{{- j}\quad\Delta\quad\omega_{T,{1\quad n}}T_{s^{t}}}{p_{n}\lbrack t\rbrack}}} \\{= {{\mathbb{e}}^{j\quad\Delta\quad\omega_{m\quad 1}{T_{s}{({t - {\overset{\sim}{t}}_{m\quad 1}})}}}\left\lbrack {{h_{m\quad 1}{p_{1}\lbrack t\rbrack}} + {\sum\limits_{n = 2}^{N}{{\mathbb{e}}^{j{({\theta_{mn} - \theta_{m\quad 1}})}}h_{mn}{p_{n}\lbrack t\rbrack}}}} \right\rbrack}} \\{= {{\mathbb{e}}^{j\quad\Delta\quad\omega_{m\quad 1}{T_{s}{({t - t_{m\quad 1}})}}}{\sum\limits_{n = 1}^{N}{{\mathbb{e}}^{j{({\theta_{mn} - \theta_{m\quad 1}})}}h_{mn}{p_{n}\lbrack t\rbrack}}}}}\end{matrix} & (20)\end{matrix}$where θ_(mn)=−Δω_(mn)T_(s){tilde over (t)}_(mn),{tilde over(t)}_(mn)=t_(mn)+Δt and Δt is random or known delay between the firstand second transmissions of the base station. Moreover, p_(n) is thetraining sequence transmitted from the n-th antenna for frequency offsetand channel estimation.

At the receive side, the users compensate for the residual frequencyoffset by using the training sequences p_(n). Then, each users mestimates via training the vector channel $\begin{matrix}{h_{m} = \begin{bmatrix}h_{m\quad 1} \\{{\mathbb{e}}^{j{({\theta_{m\quad 2} - \theta_{m\quad 1}})}}h_{m\quad 2}} \\\vdots \\{{\mathbb{e}}^{j{({\theta_{mN} - \theta_{m\quad 1}})}}h_{mN}}\end{bmatrix}} & (21)\end{matrix}$

and feeds back to the base station that computes the DIDO precoder asdescribed in the following subsection.

iii. DIDO Precoding with Pre-compensation

The base station receives the channel state information (CSI) in (12)from the users and computes the precoding weights via blockdiagonalization (BD), such thatw_(m) ^(T)h_(l)=0, ∀m≠l, m=1, . . . , M  (22)where the vectors h_(m) are defined in (21) and w_(m)=[w_(m1), w_(m2), .. . , w_(mN)]. The base station also pre-compensates for the frequencyoffset by employing the estimate in (19) and phase offset by estimatingthe delay (Δt_(o)) between the second training transmission and thecurrent transmission. Finally, the base station sends data to the usersvia the DIDO precoder.

After this transmit processing, the signal received at user i is givenby $\begin{matrix}\begin{matrix}{{r_{i}\lbrack t\rbrack} = {{\mathbb{e}}^{j\quad\Delta\quad\omega_{i\quad 1}{T_{s}{({t - {\overset{\sim}{t}}_{i\quad 1} - {\Delta\quad t_{o}}})}}}h_{i\quad 1}{\sum\limits_{m = 1}^{M}{w_{m\quad 1}{{x_{m}\lbrack t\rbrack}++}}}}} \\{\sum\limits_{n = 2}^{N}{{\mathbb{e}}^{j\quad\Delta\quad\omega_{in}{T_{s}{({t - {\overset{\sim}{t}}_{in} - {\Delta\quad t_{o}}})}}}h_{in}{\mathbb{e}}^{{- j}\quad\Delta\quad\omega_{T,{1\quad n}}{T_{s}{({t - {\Delta\quad t_{o}}})}}}{\sum\limits_{m = 1}^{M}{w_{mn}{x_{m}\lbrack t\rbrack}}}}} \\{= {{{\mathbb{e}}^{j\quad\Delta\quad\omega_{i\quad 1}{T_{s}{({t - {\Delta\quad t_{o}}})}}}{\mathbb{e}}^{{- j}\quad\Delta\quad\omega_{i\quad 1}T_{s}{\overset{\sim}{t}}_{i\quad 1}}h_{i\quad 1}{\sum\limits_{m = 1}^{M}{w_{m\quad 1}{x_{m}\lbrack t\rbrack}}}} +}} \\{\sum\limits_{n = 2}^{N}{{\mathbb{e}}^{j\quad\Delta\quad\omega_{i\quad 1}{T_{s}{({t - {\Delta\quad t_{o}}})}}}{\mathbb{e}}^{{- j}\quad\Delta\quad\omega_{in}T_{s}{\overset{\sim}{t}}_{in}}h_{in}{\sum\limits_{m = 1}^{M}{w_{mn}{x_{m}\lbrack t\rbrack}}}}} \\{= {{\gamma_{i}\lbrack t\rbrack}\left\lbrack {{h_{i\quad 1}{\sum\limits_{m = 1}^{M}{w_{m\quad 1}{x_{m}\lbrack t\rbrack}}}} + {\sum\limits_{n = 2}^{N}{{\mathbb{e}}^{j{({\theta_{in} - \theta_{i\quad 1}})}}h_{in}{\sum\limits_{m = 1}^{M}{w_{m\quad 1}{x_{m}\lbrack t\rbrack}}}}}} \right\rbrack}} \\{= {{\gamma_{i}\lbrack t\rbrack}\left\lbrack {\sum\limits_{n = 1}^{N}{{\mathbb{e}}^{j{({\theta_{in} - \theta_{i\quad 1}})}}h_{in}{\sum\limits_{m = 1}^{M}{w_{mn}{x_{m}\lbrack t\rbrack}}}}} \right\rbrack}} \\{= {{\gamma_{i}\lbrack t\rbrack}{\sum\limits_{m = 1}^{M}{\left\lbrack {\sum\limits_{n = 1}^{N}{{\mathbb{e}}^{j{({\theta_{in} - \theta_{i\quad 1}})}}h_{in}w_{mn}}} \right\rbrack{x_{m}\lbrack t\rbrack}}}}} \\{= {{\gamma_{i}\lbrack t\rbrack}{\sum\limits_{m = 1}^{M}{w_{m}^{T}h_{i}{x_{m}\lbrack t\rbrack}}}}}\end{matrix} & (23)\end{matrix}$

Where γ_(i)[n]=e^(jΔω) ^(i1) ^(T) ^(s) ^((t−{tilde over (t)}) ^(i1)^(−Δt) ^(o) ⁾. Using the property (22) we obtainr_(i)[t]=γ_(i)[t]w_(i) ^(T)h_(i)x_(i)[t]  (24)

Finally, the users compute the residual frequency offset and the channelestimation to demodulate the data streams x_(i)[t].

Results

FIG. 48 shows the SER results of DIDO 2×2 systems with and withoutfrequency offset. It is possible to see that the proposed methodcompletely cancels the frequency/phase offsets yielding the same SER assystems without offsets.

Next, we evaluate the sensitivity of the proposed compensation method tofrequency offset estimation errors and/or fluctuations of the offset intime. Hence, we re-write (14) as $\begin{matrix}{{r_{1}\lbrack t\rbrack} = {{{\mathbb{e}}^{j\quad\Delta\quad\omega_{11}{T_{s}{({t - {\overset{\sim}{t}}_{11} - {\Delta\quad t_{o}}})}}}{h_{11}\left\lbrack {{w_{11}{x_{1}\lbrack t\rbrack}} + {w_{21}{x_{2}\lbrack t\rbrack}}} \right\rbrack}} + {{\mathbb{e}}^{j\quad\Delta\quad\omega_{12}\quad{T_{s}{({t\quad - \quad{\quad\overset{\sim}{t}}_{12}\quad - \quad{\Delta\quad t_{o}}})}}}h_{12}{{\mathbb{e}}^{{- {j{({{{\Delta\quad\omega_{T}} + {2\quad\Pi}} \in})}}}\quad{T_{s}{({t\quad - {\Delta\quad t_{o}}})}}}\left\lbrack {{w_{12}{x_{1}\lbrack t\rbrack}} + {w_{22}{x_{2}\lbrack t\rbrack}}} \right\rbrack}}}} & (25)\end{matrix}$where ε indicates the estimation error and/or variation of the frequencyoffset between training and data transmission. Note that the effect of εis to destroy the orthogonality property in (13) such that theinterference terms in (14) and (16) are not completely pre-canceled atthe transmitter. As a results of that, the SER performance degrades forincreasing values of ε.

FIG. 48 shows the SER performance of the frequency offset compensationmethod for different values of ε. These results assume T_(s)=0.3 ms(i.e., signal with 3 KHz bandwidth). We observe that for E=0.001 Hz (orless) the SER performance is similar to the no offset case.

f. Description of One Embodiment of an Algorithm for Time and FrequencyOffset Estimation

Hereafter, we describe additional embodiments to carry out time andfrequency offset estimation (4701 b in FIG. 47). The transmit signalstructure under consideration is illustrated in H. Minn, V. K. Bhargava,and K. B. Letaief, “A robust timing and frequency synchronization forOFDM systems,” IEEE Trans. Wireless Commun., vol. 2, no. 4, pp. 822-839,July 2003, and studied in more detail in K. Shi and E. Serpedin, “Coarseframe and carrier synchronization of OFDM systems: a new metric andcomparison,” IEEE Trans. Wireless Commun., vol. 3, no. 4, pp. 1271-1284,July 2004. Generally sequences with good correlation properties are usedfor training. For example, for our system, Chu sequences are used whichare derived as described in D. Chu, “Polyphase codes with good periodiccorrelation properties (corresp.),” IEEE Trans. Inform. Theory, vol. 18,no. 4, pp. 531-532, July 1972. These sequences have an interestingproperty that they have perfect circular correlations. Let L_(cp) denotethe length of the cyclic prefix and let N_(t) denote the length of thecomponent training sequences. Let N_(t)=M_(t), where M_(t) is the lengthof the training sequence. Under these assumptions the transmitted symbolsequence for the preamble can be written ass[n]=t[n−N _(t)] for n=−1, . . . , −L _(cp)s[n]=t[n] for n=0, . . . , N _(t)−1s[n]=t[n−N _(t)] for n=N _(t) , . . . 2N _(t)−1s[n]=−t[n−2N _(t)] for n=2N _(t), . . . , 3N _(t)−1s[n]=t[n−3N _(t)] for n=3N _(t), . . . , 4N _(t)−1.Note that the structure of this training signal can be extended to otherlengths but repeating the block structure. For example, to use 16training signals we consider a structure such as:[CP,B,B,−B,B,B,B,−B,B,−B,−B,B,−B,B,B,−B,B,].By using this structure and letting N_(t)=4 M_(t) all the algorithms tobe described can be employed without modification. Effectively we arerepeating the training sequence. This is especially useful in caseswhere a suitable training signal may not be available.

Consider the following received signal, after matched filtering anddownsampling to the symbol rate:${r\lbrack n\rbrack} = {{{\mathbb{e}}^{{2\quad\pi}\quad \in \quad n}{\sum\limits_{l = 0}^{L}{{h\lbrack l\rbrack}{s\left\lbrack {{n - l} = \Delta} \right\rbrack}}}} + {v\lbrack n\rbrack}}$where ε is the unknown discrete-time frequency offset, Δ is the unknownframe offset, h[l] are the unknown discrete-time channel coefficients,and v[n] is additive noise. To explain the key ideas in the followingsections the presence of additive noise is ignored.

i. Coarse Frame Synchronization

The purpose of coarse frame synchronization is to solve for the unknownframe offset Δ. Let us make the following definitionsr ₁ [n]:=[r[n], r[n+1], . . . , r[n+N _(t)−1]]^(T),r ₁ [n]:=[r[n+L _(cp) ], r[n+1], . . . , r[n+N _(t)−1]]^(T),r ₂ [n]:=[r[n+N _(t) ], r[n+1+N _(t) ], . . . , r[n+2N _(t)−1]]^(T),r ₂ [n]:=[r[n+L _(cp) +N _(t) ], r[n+1+L _(cp) +N _(t) ], . . . , r[n+L_(cp)+2N _(t)−1]]^(T),r ₃ [n]:=[r[n+2N _(t) ], r[n+1+2N _(t) ], . . . , r[n+3N _(t)−1]]^(T),r ₃ [n]:=[r[n+L _(cp)+2N _(t) ], r[n+L _(cp)+1+2N _(t) ], . . . , r[n+L_(cp)+3N _(t)−1]]^(T),r ₄ [n]:=[r[n+3N _(t) ], r[n+1+3N _(t) ], . . . , r[n+4N _(t)−1]]^(T),r ₄ [n]:=[r[n+L _(cp)+3N _(t) ], r[n+L _(cp)+1+3N _(t) ], . . . , r[n+L_(cp)+4N _(t)−1]]^(T).The proposed coarse frame synchronization algorithm is inspired from thealgorithm in K. Shi and E. Serpedin, “Coarse frame and carriersynchronization of OFDM systems: a new metric and comparison,” IEEETrans. Wireless Commun., vol. 3, no. 4, pp. 1271-1284, July 2004,derived from a maximum likelihood criterion.Method 1—Improved Coarse Frame Synchronization: the Coarse FrameSynchronization Estimator Solves the Following Optimization$\hat{\Delta} = {\arg\quad{\max\limits_{k \in Z}\frac{{{P_{1}(k)}} + {{P_{2}(k)}} + {{P_{3}(k)}}}{\begin{matrix}{{\quad r_{\quad 1}}^{2} + {\quad r_{\quad 2}}^{2} + {\quad r_{\quad 3}}^{2} + {\quad r_{\quad 4}}^{2} +} \\{\frac{1}{2}\left( {{\quad{\overset{\_}{r}}_{\quad 1}}^{2} + {\quad{\overset{\_}{r}}_{\quad 2}}^{2} + {\quad{\overset{\_}{r}}_{\quad 3}}^{2} + {\quad{\overset{\_}{r}}_{\quad 4}}^{2}} \right)}\end{matrix}}}}$ where${P_{1}\lbrack k\rbrack} = {{{r_{1}^{*}\lbrack k\rbrack}{r_{2}\lbrack k\rbrack}} - {{r_{3}^{*}\lbrack k\rbrack}{r_{4}\lbrack k\rbrack}} - {{{\overset{\_}{r}}_{2}^{*}\lbrack k\rbrack}{{\overset{\_}{r}}_{3}\lbrack k\rbrack}}}$P₂[k] = r₂^(*)[k]r₄[k] − r₁^(*)[k]r₃[k]${P_{3}\lbrack k\rbrack} = {{r_{1}^{*}\lbrack k\rbrack}{{{\overset{\_}{r}}_{4}\lbrack k\rbrack}.}}$Let the corrected signal be defined asr _(c) [n]=r[n−{circumflex over (Δ)}−┌L _(cp)/4┐].The additional correction term is used to compensate for small initialtaps in the channel and can be adjusted based on the application. Thisextra delay will be included henceforth in the channel.

ii. Fractional Frequency Offset Correction

The fractional frequency offset correction follows the coarse framesynchronization block.Method 2—Improved Fractional Frequency Offset Correction: the FractionalFrequency Offset is the Solution to${\hat{\varepsilon}}_{f} = {\frac{\quad{{phaseP}_{\quad 1}\left\lbrack \quad\hat{\Delta} \right\rbrack}}{2\quad\pi\quad N_{\quad t}}.}$This is known as a fractional frequency offset because the algorithm canonly correct for offsets${{\hat{\varepsilon}}_{f}} < {\frac{1}{2\quad N_{t}}.}$This problem will be solved in the next section. Let the fine frequencyoffset corrected signal be defined asr_(f)[n]=e^(−j2π{circumflex over (ε)}) ^(f) r_(c)[n].

Note that the Methods 1 and 2 are an improvement to K. Shi and E.Serpedin, “Coarse frame and carrier synchronization of OFDM systems: anew metric and comparison,” IEEE Trans. Wireless Commun., vol. 3, no. 4,pp. 1271-1284, July 2004 that works better in frequency-selectivechannels. One specific innovation here is the use of both r and r asdescribed above. The use of r improves the prior estimator because itignores the samples that would be contaminated due to inter-symbolinterference.

iii. Integer Frequency Offset Correction

To correct for the integer frequency offset, it is necessary to write anequivalent system model for the received signal after fine frequencyoffset correction. Absorbing remaining timing errors into the channel,the received signal in the absence of noise has the following structure:${r_{f}\lbrack n\rbrack} = {{\mathbb{e}}^{j\quad 2\quad\pi\frac{nk}{N_{s}}}{\sum\limits_{l = 0}^{L_{cp}}{{g\lbrack l\rbrack}{s\left\lbrack {n - l} \right\rbrack}}}}$for n=0, 1, . . . , 4N_(t)−1. The integer frequency offset is k whilethe unknown equivalent channel is g[l].Method 3—Improved Integer Frequency Offset Correction: the IntegerFrequency Offset is the Solution to$\hat{k} = {\arg\quad{\max\limits_{{m = 0},1,\quad\ldots\quad,{N_{t} - 1}}{r*{D\lbrack k\rbrack}{S\left( {S*S} \right)}^{- 1}S*{D\lbrack k\rbrack}*r}}}$where r = D[k]Sg${D\lbrack k\rbrack}:={{diag}\left\{ {1,{\mathbb{e}}^{j\quad 2\quad\pi\frac{n\quad 1}{N_{t}}},\ldots\quad,{\mathbb{e}}^{j\quad 2\quad\pi\frac{n{({{4\quad N_{t}} - 1})}}{N_{t}}}} \right\}}$$S:=\begin{bmatrix}{s\lbrack 0\rbrack} & {s\left\lbrack {- 1} \right\rbrack} & \ldots & \ldots & {s\left\lbrack {- L_{cp}} \right\rbrack} \\{s\lbrack 1\rbrack} & {s\lbrack 0\rbrack} & {s\left\lbrack {- 1} \right\rbrack} & \ldots & {s\left\lbrack {{- L_{cp}} + 1} \right\rbrack} \\{s\left\lbrack {{4\quad N_{t}} - 1} \right\rbrack} & {s\left\lbrack {{4\quad N_{t}} - 2} \right\rbrack} & {s\left\lbrack {{4\quad N_{t}} - 3} \right\rbrack} & \ldots & {s\left\lbrack {{4\quad N_{t}} - 1 - L_{cp}} \right\rbrack}\end{bmatrix}$ $g:=\begin{bmatrix}{g\lbrack 0\rbrack} \\{g\lbrack 1\rbrack} \\\vdots \\{g\left\lbrack L_{cp} \right\rbrack}\end{bmatrix}$This gives the estimate of the total frequency offset as$\hat{\varepsilon} = {\frac{\quad\hat{k}}{\quad N_{\quad t}} + {{\quad\hat{\varepsilon}}_{f}.}}$Practically, Method 3 has rather high complexity. To reduce complexitythe following observations can be made. First of all, the product SS(S*S)⁻¹S* can be precomputed. Unfortunately, this still leaves a ratherlarge matrix multiplication. An alternative is to exploit theobservation that with the proposed training sequences, S*S≈I. This leadsto the following reduced complexity method.Method 4—Low-Complexity Improved Integer Frequency Offset Correction: aLow Complexity Integer Frequency Offset Estimator Solves$\hat{k} = {\arg\quad{\max\limits_{{m = 0},1,\quad\ldots\quad,{N_{t} - 1}}{\left( {S*{D\lbrack k\rbrack}*r} \right)*{\left( {S*{D\lbrack k\rbrack}*r} \right).}}}}$

iv. Results

In this section we compare the performance of the different proposedestimators.

First, in FIG. 50 we compare the amount of overhead required for eachmethod. Note that both of the new methods reduce the overhead requiredby 10× to 20×. To compare the performance of the different estimators,Monte Carlo experiments were performed. The setup considered is ourusual NVIS transmit waveform constructed from a linear modulation with asymbol rate of 3K symbols per second, corresponding to a passbandbandwidth of 3 kHz, and raised cosine pulse shaping. For each MonteCarlo realization, the frequency offset is generated from a uniformdistribution on [−f_(max), f_(max)].

A simulation with a small frequency offset of f_(max)=2 Hz and nointeger offset correction is illustrated in FIG. 51. It can be seen fromthis performance comparison that performance with N_(t)/M_(t)=1 isslightly degraded from the original estimator, though stillsubstantially reduces overhead. Performance with N_(t)/M_(t)=4 is muchbetter, almost 10 dB. All the curves experience a knee at low SNR pointsdue to errors in the integer offset estimation. A small error in theinteger offset can create a large frequency error and thus a large meansquared error. Integer offset correction can be turned off in smalloffsets to improve performance.

In the presence of multipath channels, the performance of frequencyoffset estimators generally degrades. Turning off the integer offsetestimator, however, reveals quite good performance in FIG. 52. Thus, inmultipath channels it is even more important to perform a robust coarsecorrection followed by an improved fine correction algorithm. Noticethat the offset performance with N_(t)/M_(t)=4 is much better in themultipath case.

Embodiments of the invention may include various steps as set forthabove. The steps may be embodied in machine-executable instructionswhich cause a general-purpose or special-purpose processor to performcertain steps. For example, the various components within the BaseStations/APs and Client Devices described above may be implemented assoftware executed on a general purpose or special purpose processor. Toavoid obscuring the pertinent aspects of the invention, various wellknown personal computer components such as computer memory, hard drive,input devices, etc., have been left out of the figures.

Alternatively, in one embodiment, the various functional modulesillustrated herein and the associated steps may be performed by specifichardware components that contain hardwired logic for performing thesteps, such as an application-specific integrated circuit (“ASIC”) or byany combination of programmed computer components and custom hardwarecomponents.

In one embodiment, certain modules such as the Coding, Modulation andSignal Processing Logic 903 described above may be implemented on aprogrammable digital signal processor (“DSP”) (or group of DSPs) such asa DSP using a Texas Instruments' TMS320x architecture (e.g., aTMS320C6000, TMS320C5000, . . . etc). The DSP in this embodiment may beembedded within an add-on card to a personal computer such as, forexample, a PCI card. Of course, a variety of different DSP architecturesmay be used while still complying with the underlying principles of theinvention.

Elements of the present invention may also be provided as amachine-readable medium for storing the machine-executable instructions.The machine-readable medium may include, but is not limited to, flashmemory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs,magnetic or optical cards, propagation media or other type ofmachine-readable media suitable for storing electronic instructions. Forexample, the present invention may be downloaded as a computer programwhich may be transferred from a remote computer (e.g., a server) to arequesting computer (e.g., a client) by way of data signals embodied ina carrier wave or other propagation medium via a communication link(e.g., a modem or network connection).

Throughout the foregoing description, for the purposes of explanation,numerous specific details were set forth in order to provide a thoroughunderstanding of the present system and method. It will be apparent,however, to one skilled in the art that the system and method may bepracticed without some of these specific details. Accordingly, the scopeand spirit of the present invention should be judged in terms of theclaims which follow.

Moreover, throughout the foregoing description, numerous publicationswere cited to provide a more thorough understanding of the presentinvention. All of these cited references are incorporated into thepresent application by reference.

1. A method for dynamically adapting the communication characteristicsof a multiple antenna system (MAS) with multi-user (MU) transmissions(“MU-MAS”): transmitting a training signal from each antenna of a basestation to each of a plurality of wireless client devices, each of theclient devices analyzing each training signal to generate channelcharacterization data, and receiving the channel characterization dataat the base station; determining the instantaneous or statisticalchannel quality (“link quality metric”) for the wireless client devicesusing the channel characterization data; determining a subset of usersand a MU-MAS transmission mode based on the link quality metric;computing a plurality of MU-MAS precoder weights based on the channelcharacterization data; precoding data using the MU-MAS precoder weightsto generate precoded data signals for each antenna of the base station;and transmitting the precoded data signals through each antenna of thebase station to each respective client device within the selectedsubset.
 2. The method as in claim 1 wherein the MU-MAS transmissionmodes includes different combinations of antenna selection/diversity ormultiplexing, modulation/coding schemes (MCSs) and arrayconfigurations/geometries.
 3. The method as in claim 1 wherein the linkquality metric is estimated in the time, frequency and/or space domains.4. The method as in claim 1 wherein the link quality metric includes thesignal to noise ratio (SNR) of the signals received at the clientdevices.
 5. The method of claim 1 wherein the MU-MAS system is adistributed-input distributed-output (DIDO) communication system andwherein the MU-MAS transmission mode is a DIDO transmission mode basedon the link quality metric and wherein the MU-MAS precoder weights areDIDO precoder weights.
 6. A system for dynamically adapting thecommunication characteristics of a MU-MAS communication systemcomprising: one or more coding modulation units to encode and modulateinformation bits for each of a plurality of wireless client devices toproduce encoded and modulated information bits; one or more mappingunits to map the encoded and modulated information bits to complexsymbols; and a MU-MAS configurator unit to determine a subset of usersand a MU-MAS transmission mode based on channel characterization dataobtained through feedback from the wireless client devices and toresponsively control the coding modulation units and mapping units. 7.The system as in claim 6 further comprising: a MU-MAS precoding unitoperating under control of the MU-MAS configurator unit to computeprecoding weights for precoding data signals prior to transmission tothe client devices.
 8. The system as in claim 7 further comprising: oneor more orthogonal frequency division multiplexing (OFDM) units toreceive the precoded signals from the precoding unit and to modulate theprecoded signals according to an OFDM standard.
 9. The system as inclaim 8 wherein the OFDM standard comprises computing an inverse fastFourier transform (IFFT) and adding a cyclic prefix.
 10. The system asin claim 8 further comprising one or more D/A units to perform digitalto analog (D/A) conversion on the output of the OFDM units to generatean analog baseband signal; and one or more radio frequency (RF) units toupconvert the baseband signal to radio frequency and transmit thesignals using a corresponding one or more transmit antennas.
 11. Thesystem as in claim 7 wherein the MU-MAS precoding unit is implemented asa minimum mean square error (MMSE), a weighted MMSE, a zero-forcing (ZF)or a block diagonalization (BD) precoder.
 12. The system as in claim 6wherein the MU-MAS system is a DIDO system and wherein the MU-MASconfigurator unit is a DIDO configurator unit to determine a subset ofusers and a DIDO transmission mode based on channel characterizationdata obtained through feedback from the wireless client devices and toresponsively control the coding modulation units and mapping units. 13.A wireless client device for use in a system for dynamically adaptingthe communication characteristics of a MU-MAS communication systemcomprising: one or more RF units to receive signals transmitted from oneor more MU-MAS transmitter units, and downconvert the signals tobaseband; one or more analog to digital (A/D) conversion units toreceive the downconverted signals and to convert the signals from analogsignals to digital signals; one or more OFDM units to remove cyclicprefix and perform a fast Fourier transform (FFT) on the digital signalsto report the signals in the frequency domain; a channel estimator whichreceives a signal output from the one or more OFDM units during atraining period and responsively calculates the link quality metrics;and a feedback generator unit to transmit the link quality metrics to abase station for use in modulation/coding, precoding signals and userselection prior to transmission to the wireless client device.
 14. Thewireless client device as in claim 13 where the channel estimates arecomputed in the time domain by using the input to the OFDM units
 15. Thewireless client device as in claim 13 wherein the feedback generatorunit further comprises logic for quantizing the channel estimates and orlink-quality metrics prior to transmission to the base station.
 16. Thewireless client device as in claim 13 further comprising: a receiverunit which receives outputs from the OFDM units and responsivelydemodulates/decodes the signal to obtain the estimate of the transmitteddata.
 17. The wireless client device as in claim 15 wherein the receiverunit is a minimum mean square error (MMSE) receiver, zero-forcing (ZF)receiver, a maximum likelihood (ML) or a MAP receiver.
 18. The system orwireless client as in claims 6 or 14 wherein the MU-MAS configuratorunit or channel estimator unit, respectively, employs polarizationand/or pattern diversity techniques as a means to reduce the array size,while obtaining diversity over the wireless link.
 19. The system orwireless client as in claims 6 or 13 wherein communication occurs viaNVIS and/or groundwave as a means to increase diversity and downlinkthroughput.
 20. The system or wireless client as in claims 6 or 13,respectively, wherein pattern diversity are employed to communicate viaground-wave with certain users and NVIS with other users.
 21. The systemor wireless client as in claim 20 wherein each client exploits spatialseparation of the ground-wave and NVIS links as a means to increase thespatial diversity of the link.
 22. A system as in claim 6 furthercomprising a Base Station which adaptively switches between differentarray geometries and different antenna diversity techniques based on thechannel quality feedback from the clients as a means to increase thediversity of the links and the downlink throughput.
 23. A system as inclaim 6 further comprising a Base Station which defines groups of usersand schedules different sets of users for transmission based on relativepriority and/or channel condition.